The document discusses independent and repeated samples. An independent sample involves subjects or observations from one sample that have no relationship with subjects or observations from another sample. A repeated sample involves measuring the same subjects or matched subjects more than once. An example is given of a study measuring sleep hours in the same subjects before and after starting a dietary regimen to examine the impact of the regimen over time. This uses a repeated sample as the same subjects are measured on multiple occasions.
Are the samples repeated or independent or bothKen Plummer
The document discusses independent and repeated samples. An independent sample involves collecting data from two unrelated groups, like ACT scores from Texas students and national students. A repeated sample involves collecting data from the same group on multiple occasions, like measuring vocabulary test scores of the same group of younger and older people. The key to independent samples is that the members of one group cannot be part of the other, while repeated samples involve measuring the same individuals in each group.
The document presents 4 problems and classifies each as a different statistical question type: difference, goodness of fit, relationship, or independence. The problems involve classifying high school athletes' pizza consumption, the distribution of baseball cards in packs, the relationship between professors' years of teaching and publishing with income, and whether government funding of studies is independent of the studies' political perspectives. For each problem, the document explains the classification and provides a rationale.
The document discusses dependent variables in statistics problems. It explains that the dependent variable is the "effect" side of a cause-and-effect relationship, or the "influenced" side of an influencer-influenced relationship. Several examples are provided to demonstrate how to identify the dependent variable in word problems involving research studies. The dependent variable is the main outcome or variable being measured in response to changes in other variables. Problems can have either a single dependent variable or multiple dependent variables.
The document discusses covariates and how to determine if they are present in a problem. It uses an example of examining the relationship between socioeconomic status (SES) and student ACT scores, and whether parental status is a covariate. Adding parental status as a variable decreases the correlation between SES and ACT scores, showing it plays a role in explaining their relationship. Controlling for covariates allows viewing the unique relationship between variables when the covariate's effect is removed. Key terms that indicate a covariate is present include "control for", "hold constant", and "adjust for".
This document provides an overview of group difference methods, specifically analysis of covariance (ANCOVA). ANCOVA is a statistical technique used to control for the influence of covariates (variables related to but not manipulated in an experiment) when assessing the differences between groups on a dependent variable. The document outlines the basic ANCOVA model and assumptions, including that the relationship between covariates and dependent variable is linear and homogeneous across groups. It also discusses how ANCOVA partitions variability, its usage to control for non-randomized but measurable factors, and limitations such as small number of covariates and independence from treatment.
How many dependent variables (practice)Ken Plummer
The document presents 4 practice problems involving dependent variables. For each problem, there is one dependent variable being examined:
1) GPA in relation to choosing lunch period friends
2) Levels of depression in relation to religious beliefs
3) Suicidal thoughts and feelings of shame in relation to grade level
4) College acceptance rates in relation to gender
For all problems, the dependent variable(s) would be influenced by or dependent on the independent variable presented in each scenario.
The document discusses levels in statistics and provides examples to illustrate the concept. Levels refer to the number of conditions within an independent variable. The number of levels determines the appropriate statistical analysis method. Examples are provided of studies with different numbers of levels, such as socioeconomic status having 4 levels (wealthy, upper middle class, lower middle class, below poverty line) while gender has 2 levels (male, female). Visual representations are given to depict levels within independent variables. The document concludes by restating that levels indicate the number of conditions in an independent variable and that determining the number of levels is important for selecting the correct statistical analysis.
The document discusses conducting a factorial analysis of variance (ANOVA) to analyze the effects of two independent variables, athlete type (football, basketball, soccer players) and age (adults vs teenagers), on the dependent variable of number of slices of pizza consumed. It outlines setting up a 2x3 factorial design to compare the six groups that results from the two independent variables, each with multiple levels, and determining that a factorial ANOVA is the appropriate statistical analysis for this research question and study design.
Are the samples repeated or independent or bothKen Plummer
The document discusses independent and repeated samples. An independent sample involves collecting data from two unrelated groups, like ACT scores from Texas students and national students. A repeated sample involves collecting data from the same group on multiple occasions, like measuring vocabulary test scores of the same group of younger and older people. The key to independent samples is that the members of one group cannot be part of the other, while repeated samples involve measuring the same individuals in each group.
The document presents 4 problems and classifies each as a different statistical question type: difference, goodness of fit, relationship, or independence. The problems involve classifying high school athletes' pizza consumption, the distribution of baseball cards in packs, the relationship between professors' years of teaching and publishing with income, and whether government funding of studies is independent of the studies' political perspectives. For each problem, the document explains the classification and provides a rationale.
The document discusses dependent variables in statistics problems. It explains that the dependent variable is the "effect" side of a cause-and-effect relationship, or the "influenced" side of an influencer-influenced relationship. Several examples are provided to demonstrate how to identify the dependent variable in word problems involving research studies. The dependent variable is the main outcome or variable being measured in response to changes in other variables. Problems can have either a single dependent variable or multiple dependent variables.
The document discusses covariates and how to determine if they are present in a problem. It uses an example of examining the relationship between socioeconomic status (SES) and student ACT scores, and whether parental status is a covariate. Adding parental status as a variable decreases the correlation between SES and ACT scores, showing it plays a role in explaining their relationship. Controlling for covariates allows viewing the unique relationship between variables when the covariate's effect is removed. Key terms that indicate a covariate is present include "control for", "hold constant", and "adjust for".
This document provides an overview of group difference methods, specifically analysis of covariance (ANCOVA). ANCOVA is a statistical technique used to control for the influence of covariates (variables related to but not manipulated in an experiment) when assessing the differences between groups on a dependent variable. The document outlines the basic ANCOVA model and assumptions, including that the relationship between covariates and dependent variable is linear and homogeneous across groups. It also discusses how ANCOVA partitions variability, its usage to control for non-randomized but measurable factors, and limitations such as small number of covariates and independence from treatment.
How many dependent variables (practice)Ken Plummer
The document presents 4 practice problems involving dependent variables. For each problem, there is one dependent variable being examined:
1) GPA in relation to choosing lunch period friends
2) Levels of depression in relation to religious beliefs
3) Suicidal thoughts and feelings of shame in relation to grade level
4) College acceptance rates in relation to gender
For all problems, the dependent variable(s) would be influenced by or dependent on the independent variable presented in each scenario.
The document discusses levels in statistics and provides examples to illustrate the concept. Levels refer to the number of conditions within an independent variable. The number of levels determines the appropriate statistical analysis method. Examples are provided of studies with different numbers of levels, such as socioeconomic status having 4 levels (wealthy, upper middle class, lower middle class, below poverty line) while gender has 2 levels (male, female). Visual representations are given to depict levels within independent variables. The document concludes by restating that levels indicate the number of conditions in an independent variable and that determining the number of levels is important for selecting the correct statistical analysis.
The document discusses conducting a factorial analysis of variance (ANOVA) to analyze the effects of two independent variables, athlete type (football, basketball, soccer players) and age (adults vs teenagers), on the dependent variable of number of slices of pizza consumed. It outlines setting up a 2x3 factorial design to compare the six groups that results from the two independent variables, each with multiple levels, and determining that a factorial ANOVA is the appropriate statistical analysis for this research question and study design.
The document discusses questions of independence, which examine whether changes in one variable are related to or independent of changes in another variable. It provides examples of determining if two variables, such as number of cigarette packs smoked per day and cognitive impairment, or anger survey scores and number of racing accidents, are independent. The goal is to analyze data sets to see if higher or lower scores on one variable are unrelated to groups or values on the other variable.
The document discusses independent and dependent variables in statistics. It explains that the independent variable is the cause or influencer in a study, such as amount of study time or amount of sleep, while the dependent variable is the effect or what is being influenced, such as test scores. It provides examples of how to identify the independent and dependent variables in studies and word problems about viral infections, background noise, and more.
The document discusses four types of inferential statistical methods, beginning with questions of difference. Questions of difference ask if one group is different from, similar to, or comparable to another group based on some outcome. Examples are provided, including comparing driving speed between women and men, and texting while driving between teenagers and adults. The document also provides an example comparing three groups: tweens, teenagers, and college freshmen in terms of time spent on electronics. Finally, an example is given of looking at similarities between two groups by comparing GRE verbal scores of a sample of graduate students to the national average.
The document discusses questions of relationship, which focus on how variables co-vary or correlate with each other. It provides an equation to show that an increase or decrease in variable 1 is accompanied by an increase or decrease in variable 2. As an example, researchers hypothesize that as temperature increases, burglaries increase. Monthly temperature and burglary data is presented and ranked to illustrate that the relative ranks of the two variables are the same, showing a direct relationship between temperature and burglaries.
A pizza café owner wants to determine how much inventory is needed during football and basketball seasons based on how many slices of pizza each group eats. After collecting data showing outliers among basketball players, a Mann Whitney U test will be used. The null hypothesis would state that there is no statistically significant difference between the median slices of pizza eaten by football players and basketball players.
The document discusses different scales of measurement used in research. There are four main scales: nominal, ordinal, interval, and ratio. Nominal scales use numbers to replace categories or names and assume no quantitative relationship between numbers. Ordinal scales represent relative quantities of attributes but intervals between numbers are not equal. Interval and ratio scales both assume equal intervals but ratio scales have a true zero point.
Rank order relationship (ties) practice problemsKen Plummer
The director of a health clinic asked to determine the relationship between patients' age and their systolic blood pressure or nationally ranked blood pressure. Kendall's Tau is used when there are ties among the ranked variables, while Spearman's Rho is used when there are no ties. In problems 1, 3, and 4 there were ties, so Kendall's Tau was the appropriate test, while in problem 2 there were no ties so Spearman's Rho was suitable.
4. Calculate samplesize for cross-sectional studiesAzmi Mohd Tamil
This document discusses sample size calculations for a comparative cross-sectional study to prove an association between a risk factor and outcome. It provides an example calculating the sample size needed to show Indians have a higher risk of diabetes compared to other races in Malaysia. The calculations are shown manually and using online calculators StatCalc and PS2. While the manual and StatCalc methods agree, PS2 produces a different result. Prior literature on disease rates and the risk factor is needed for sample size calculations.
If at least one sample size is greater than 30, use a parametric test even if the distribution is skewed, as probability density, normal distributions, sampling distributions, and standard error concepts allow for this. If the sample size is less than 30 and the distribution is skewed, use a non-parametric test to analyze the data, as these same statistical concepts require it.
Running & Reporting an One-way ANCOVA in SPSSKen Plummer
Administrators at Parday University asked researchers to study differences in average sleep hours among freshmen, sophomores, juniors, and seniors, controlling for gender. The researchers hypothesized there would be a significant difference in sleep hours between freshmen at the beginning and end of the semester after accounting for gender. An analysis of covariance (ANCOVA) was conducted with year in school as the independent variable, average sleep hours as the dependent variable, and gender as the covariate. The ANCOVA results showed a significant difference in average sleep hours among years in school after removing the effect of gender, but gender was not a significant covariate.
Non parametric relationship (names) - practice problemsKen Plummer
The document describes 5 practice problems involving determining the appropriate correlation coefficient to quantify relationships between variables. For problems involving a continuous variable and an ordinal or skewed continuous variable with no ties, Spearman's Rho is appropriate. When the skewed continuous variable has ties, Kendall's Tau is suitable. Point-biserial correlation is used when one variable is dichotomous and the other is continuous. Phi coefficient examines relationships between two dichotomous variables.
Parametric tests assume variables are normally distributed but this is sometimes untrue. Non-parametric tests like the Mann-Whitney U test can be used instead as they do not require normal distributions. The Mann-Whitney U test is analogous to the independent samples t-test but uses medians instead of means, making it not sensitive to outliers. It operates on subjects' rank positions rather than differences from the mean.
ANOVA, ANCOVA, MANOVA, and MANCOVA are statistical analyses used to test differences between groups.
ANOVA tests for differences between 2 or more means and partitions variances into sums of squares between and within groups. ANCOVA controls for additional factors, called covariates, to reduce error and increase power.
MANOVA assesses the effect of independent variables on multiple dependent variables simultaneously, accounting for correlation between variables. It tests for overall differences using a multivariate F value. Univariate follow-ups can then examine differences on each individual dependent variable.
MANCOVA extends MANOVA to include controlling for covariates, allowing evaluation of changes in dependent variables while accounting for additional continuous factors measured at different
Quickreminder nature of the data (relationship)Ken Plummer
This document provides guidance on which statistical tests to use when analyzing different variable types. It recommends using the phi coefficient for dichotomous by dichotomous variables, point-biserial for dichotomous by scaled variables, Spearman's rho for ordinal by any other variable or scaled by scaled with one variable skewed and less than 30 subjects, and Kendall's tau for ordinal with ties by any other variable or scaled by scaled with one variable skewed and less than 30 subjects with ties.
This document discusses descriptive and inferential statistics. Descriptive statistics describe what is occurring in an entire population, using words like "all" or "everyone". Inferential statistics draw conclusions about a larger population based on a sample, since observing the entire population is often not feasible. The document provides examples to illustrate the difference, such as determining average test scores for all students versus using a sample of scores to estimate averages for an entire state.
The document describes a study conducted by a pizza café owner to determine which type of high school athlete to market to. The owner measured the ounces of pizza consumed by 12 football players, 12 basketball players, and 12 soccer players. The owner also surveyed each athlete's preference for pizza prior to the study. The document discusses that an analysis of covariance (ANCOVA) is needed to control for the covariate of pizza preference when comparing pizza consumption between the athlete groups.
Nonparametric tests use the median instead of the mean to calculate differences between groups. These tests have parametric analogues that use the mean. Some common nonparametric tests include the Wilcoxon test, Mann-Whitney U test, and Kruskal-Wallis test, which correspond to the t-test, independent samples t-test, and one-way ANOVA respectively. Z-tests are also nonparametric and used to compare proportions between samples and populations or between two samples.
This document discusses the null hypothesis for a one-way analysis of covariance (ANCOVA). It explains that a one-way ANCOVA compares the influence of an independent variable with at least two levels on a dependent variable, while controlling for the effect of a covariate. The document provides a template for writing the null hypothesis, which states that there is no significant effect of the independent variable on the dependent variable when controlling for the covariate. It gives two examples applying this template.
The document discusses normal and skewed distributions. It provides an example of student study hours to illustrate how to create a distribution from a data set. The distribution plots hours of study on the x-axis and number of occurrences on the y-axis. It then calculates the mean of the example data set to demonstrate that the mean describes the center point of a normal distribution when the majority of the data is in the middle with decreasing amounts towards the tails.
Analysis of covariance (ANCOVA) is a statistical test that assesses whether the means of a dependent variable are equal across levels of a categorical independent variable while statistically controlling for the effects of other continuous variables known as covariates. ANCOVA works by adjusting the sums of squares for the independent variable to remove the influence of the covariate. This allows ANCOVA to test for differences between groups while controlling for the influence of other continuous variables. The assumptions of ANCOVA include those of ANOVA as well as the assumptions that the relationship between the dependent variable and covariate is linear and the same across all groups.
Inferring or describing - practice problemsKen Plummer
The document presents 5 practice problems that differentiate between inferring and describing. The problems involve analyzing census data to determine education levels, surveying parents to gauge satisfaction with school counseling, identifying common blood pressure readings in a dataset, examining census data on foreign language use, and generalizing reading comprehension test results from a sample of students to an entire school district.
The document presents a series of practice problems about differentiating between scaled, ordinal, nominal, and proportional data. The problems describe hypothetical studies and ask the reader to identify which type of data is being examined. For example, one problem discusses comparing the percentage of residents below the poverty line in a school district to the national percentage, which involves proportional data. The document provides the options for each problem's data type, an explanation of the right answer, and advances to the next problem for additional practice differentiation data types.
The document discusses questions of independence, which examine whether changes in one variable are related to or independent of changes in another variable. It provides examples of determining if two variables, such as number of cigarette packs smoked per day and cognitive impairment, or anger survey scores and number of racing accidents, are independent. The goal is to analyze data sets to see if higher or lower scores on one variable are unrelated to groups or values on the other variable.
The document discusses independent and dependent variables in statistics. It explains that the independent variable is the cause or influencer in a study, such as amount of study time or amount of sleep, while the dependent variable is the effect or what is being influenced, such as test scores. It provides examples of how to identify the independent and dependent variables in studies and word problems about viral infections, background noise, and more.
The document discusses four types of inferential statistical methods, beginning with questions of difference. Questions of difference ask if one group is different from, similar to, or comparable to another group based on some outcome. Examples are provided, including comparing driving speed between women and men, and texting while driving between teenagers and adults. The document also provides an example comparing three groups: tweens, teenagers, and college freshmen in terms of time spent on electronics. Finally, an example is given of looking at similarities between two groups by comparing GRE verbal scores of a sample of graduate students to the national average.
The document discusses questions of relationship, which focus on how variables co-vary or correlate with each other. It provides an equation to show that an increase or decrease in variable 1 is accompanied by an increase or decrease in variable 2. As an example, researchers hypothesize that as temperature increases, burglaries increase. Monthly temperature and burglary data is presented and ranked to illustrate that the relative ranks of the two variables are the same, showing a direct relationship between temperature and burglaries.
A pizza café owner wants to determine how much inventory is needed during football and basketball seasons based on how many slices of pizza each group eats. After collecting data showing outliers among basketball players, a Mann Whitney U test will be used. The null hypothesis would state that there is no statistically significant difference between the median slices of pizza eaten by football players and basketball players.
The document discusses different scales of measurement used in research. There are four main scales: nominal, ordinal, interval, and ratio. Nominal scales use numbers to replace categories or names and assume no quantitative relationship between numbers. Ordinal scales represent relative quantities of attributes but intervals between numbers are not equal. Interval and ratio scales both assume equal intervals but ratio scales have a true zero point.
Rank order relationship (ties) practice problemsKen Plummer
The director of a health clinic asked to determine the relationship between patients' age and their systolic blood pressure or nationally ranked blood pressure. Kendall's Tau is used when there are ties among the ranked variables, while Spearman's Rho is used when there are no ties. In problems 1, 3, and 4 there were ties, so Kendall's Tau was the appropriate test, while in problem 2 there were no ties so Spearman's Rho was suitable.
4. Calculate samplesize for cross-sectional studiesAzmi Mohd Tamil
This document discusses sample size calculations for a comparative cross-sectional study to prove an association between a risk factor and outcome. It provides an example calculating the sample size needed to show Indians have a higher risk of diabetes compared to other races in Malaysia. The calculations are shown manually and using online calculators StatCalc and PS2. While the manual and StatCalc methods agree, PS2 produces a different result. Prior literature on disease rates and the risk factor is needed for sample size calculations.
If at least one sample size is greater than 30, use a parametric test even if the distribution is skewed, as probability density, normal distributions, sampling distributions, and standard error concepts allow for this. If the sample size is less than 30 and the distribution is skewed, use a non-parametric test to analyze the data, as these same statistical concepts require it.
Running & Reporting an One-way ANCOVA in SPSSKen Plummer
Administrators at Parday University asked researchers to study differences in average sleep hours among freshmen, sophomores, juniors, and seniors, controlling for gender. The researchers hypothesized there would be a significant difference in sleep hours between freshmen at the beginning and end of the semester after accounting for gender. An analysis of covariance (ANCOVA) was conducted with year in school as the independent variable, average sleep hours as the dependent variable, and gender as the covariate. The ANCOVA results showed a significant difference in average sleep hours among years in school after removing the effect of gender, but gender was not a significant covariate.
Non parametric relationship (names) - practice problemsKen Plummer
The document describes 5 practice problems involving determining the appropriate correlation coefficient to quantify relationships between variables. For problems involving a continuous variable and an ordinal or skewed continuous variable with no ties, Spearman's Rho is appropriate. When the skewed continuous variable has ties, Kendall's Tau is suitable. Point-biserial correlation is used when one variable is dichotomous and the other is continuous. Phi coefficient examines relationships between two dichotomous variables.
Parametric tests assume variables are normally distributed but this is sometimes untrue. Non-parametric tests like the Mann-Whitney U test can be used instead as they do not require normal distributions. The Mann-Whitney U test is analogous to the independent samples t-test but uses medians instead of means, making it not sensitive to outliers. It operates on subjects' rank positions rather than differences from the mean.
ANOVA, ANCOVA, MANOVA, and MANCOVA are statistical analyses used to test differences between groups.
ANOVA tests for differences between 2 or more means and partitions variances into sums of squares between and within groups. ANCOVA controls for additional factors, called covariates, to reduce error and increase power.
MANOVA assesses the effect of independent variables on multiple dependent variables simultaneously, accounting for correlation between variables. It tests for overall differences using a multivariate F value. Univariate follow-ups can then examine differences on each individual dependent variable.
MANCOVA extends MANOVA to include controlling for covariates, allowing evaluation of changes in dependent variables while accounting for additional continuous factors measured at different
Quickreminder nature of the data (relationship)Ken Plummer
This document provides guidance on which statistical tests to use when analyzing different variable types. It recommends using the phi coefficient for dichotomous by dichotomous variables, point-biserial for dichotomous by scaled variables, Spearman's rho for ordinal by any other variable or scaled by scaled with one variable skewed and less than 30 subjects, and Kendall's tau for ordinal with ties by any other variable or scaled by scaled with one variable skewed and less than 30 subjects with ties.
This document discusses descriptive and inferential statistics. Descriptive statistics describe what is occurring in an entire population, using words like "all" or "everyone". Inferential statistics draw conclusions about a larger population based on a sample, since observing the entire population is often not feasible. The document provides examples to illustrate the difference, such as determining average test scores for all students versus using a sample of scores to estimate averages for an entire state.
The document describes a study conducted by a pizza café owner to determine which type of high school athlete to market to. The owner measured the ounces of pizza consumed by 12 football players, 12 basketball players, and 12 soccer players. The owner also surveyed each athlete's preference for pizza prior to the study. The document discusses that an analysis of covariance (ANCOVA) is needed to control for the covariate of pizza preference when comparing pizza consumption between the athlete groups.
Nonparametric tests use the median instead of the mean to calculate differences between groups. These tests have parametric analogues that use the mean. Some common nonparametric tests include the Wilcoxon test, Mann-Whitney U test, and Kruskal-Wallis test, which correspond to the t-test, independent samples t-test, and one-way ANOVA respectively. Z-tests are also nonparametric and used to compare proportions between samples and populations or between two samples.
This document discusses the null hypothesis for a one-way analysis of covariance (ANCOVA). It explains that a one-way ANCOVA compares the influence of an independent variable with at least two levels on a dependent variable, while controlling for the effect of a covariate. The document provides a template for writing the null hypothesis, which states that there is no significant effect of the independent variable on the dependent variable when controlling for the covariate. It gives two examples applying this template.
The document discusses normal and skewed distributions. It provides an example of student study hours to illustrate how to create a distribution from a data set. The distribution plots hours of study on the x-axis and number of occurrences on the y-axis. It then calculates the mean of the example data set to demonstrate that the mean describes the center point of a normal distribution when the majority of the data is in the middle with decreasing amounts towards the tails.
Analysis of covariance (ANCOVA) is a statistical test that assesses whether the means of a dependent variable are equal across levels of a categorical independent variable while statistically controlling for the effects of other continuous variables known as covariates. ANCOVA works by adjusting the sums of squares for the independent variable to remove the influence of the covariate. This allows ANCOVA to test for differences between groups while controlling for the influence of other continuous variables. The assumptions of ANCOVA include those of ANOVA as well as the assumptions that the relationship between the dependent variable and covariate is linear and the same across all groups.
Inferring or describing - practice problemsKen Plummer
The document presents 5 practice problems that differentiate between inferring and describing. The problems involve analyzing census data to determine education levels, surveying parents to gauge satisfaction with school counseling, identifying common blood pressure readings in a dataset, examining census data on foreign language use, and generalizing reading comprehension test results from a sample of students to an entire school district.
The document presents a series of practice problems about differentiating between scaled, ordinal, nominal, and proportional data. The problems describe hypothetical studies and ask the reader to identify which type of data is being examined. For example, one problem discusses comparing the percentage of residents below the poverty line in a school district to the national percentage, which involves proportional data. The document provides the options for each problem's data type, an explanation of the right answer, and advances to the next problem for additional practice differentiation data types.
The document presents 7 practice problems about calculating different statistics from data sets. The problems involve comparing average cyberbullying incidents between grade levels, comparing test score ranges between therapy groups, calculating average blood pressure, examining exam score distributions, determining income variation across a school district, and describing comfort level distributions for different faculty groups. Central tendency, spread, and symmetry are the key statistical concepts addressed.
A student who didnt study for the upcoming quiz decides to wing .docxransayo
A student who didn't study for the upcoming quiz decides to 'wing it' and just guess on the 10 question quiz. Every question is True/False. What is the probability that his grade on the quiz will be at most 50%?
Please express your answer as a percent rounded to the hundredths decimal place. Include the '%' symbol.
A student who didn't study for the upcoming quiz decides to 'wing it' and just guess on the 10 question quiz. Every question has 5 choices (a - e). What is the probability that his grade on the quiz will be at most 50%?
Please express your answer as a percent rounded to the hundredths decimal place. Include the '%' symbol.
In a certain college, 33% of the physics majors belong to ethnic minorities. If 10 students are selected at random from the physics majors, that is the probability that no more than 6 belong to an ethnic minority?
Round your answer to four decimal places.
Find the mean , µ, for a bionomial distribution where n = 50 and p = .175.
Round the answer to the hundredths decimal place.
Find the mean , µ, for a bionomial distribution where n = 125 and p = 0.47
Round the answer to the hundredths decimal place.
Find the standard deviation for a bionomial distribution where n = 125 and p = 0.47.
Round the answer to the hundredths decimal place.
Find the standard deviation for a bionomial distribution where n = 50 and p = .175.
Round the answer to the hundredths decimal place.
A bank's loan officer rates applicants for credit. The ratings are normally distributed with a mean of 200 and a standard deviation of 50. If an applicant is randomly selected, find the probability of a rating that is between 200 and 275.
Your answer should be a decimal rounded to the fourth decimal place.
A bank's loan officer rates applicants for credit. The ratings are normally distributed with a mean of 200 and a standard deviation of 50. If an applicant is randomly selected, find the probability of a rating that is between 170 and 220.
Your answer should be a decimal rounded to the fourth decimal place.
The volumes of soda in quart soda bottles are normally distributed with a mean of 32.3 oz and a standard deviation of 1.2 oz. What is the probability that the volume of soda in a randomly selected bottle will be less than 32 oz?
Your answer should be a decimal rounded to the fourth decimal place.
The amount of rainfall in January in a certain city is normally distributed with a mean of 4.6 inches and a standard deviation of 0.3 inches. Find the value of the first quartile Q1.
Round your answer to the nearest tenth.
In one region, the September energy consumption levels for single-family homes are found to be normally distributed with a mean of 1050 kWh and a standard deviation of 218 kWh. Find P45, which is the consumption level separating the bottom 45% from the top 55%.
Round your answer to the nearest tenth.
Scores on a test have a mean of 73 and Q3 is 83. The scores have a distribution that is approximatel.
Central tendency, shape, or symmetry practice problems (2)Ken Plummer
The document presents 5 practice problems about differentiating between concepts of central tendency, spread, and symmetry in statistics. For each problem, the user is asked to identify which statistical concept is being examined based on a description. The concepts are then explained, with central tendency referring to average or middle values, spread referring to the differences between lowest and highest values, and symmetry referring to the shape or distribution of data.
Is the data nominal tallied, or ordinal (ranked)?Ken Plummer
The document discusses different types of data that may be used in statistical tests, including ordinal (ranked) data and nominal tallied data. It provides examples of how ordinal data could be expressed as rankings or percentiles. Nominal tallied data refers to counts of observations in different categories or levels. The document suggests that if a problem contains at least one ordinal variable or two nominal variables, a non-parametric test may be appropriate to test for independence between the variables.
Reporting chi square goodness of fit test of independence in apaKen Plummer
A chi-square goodness of fit test was used to analyze data from a public opinion poll of 1000 voters in Connecticut on their party affiliation. The expected distribution was 40% Republican and 60% Democrat, but the observed results were 32% Republican and 68% Democrat. A sample report in APA style for these results includes the chi-square value, degrees of freedom, and p-value to determine if there is a significant deviation from the expected distribution.
The document discusses central tendency and skewness. In Demo #1, it explains that the median is the best measure of central tendency for a positively skewed distribution because it is not influenced by outliers. In Demo #2, it states the mode is best for a multimodal distribution because it indicates the most frequent values. Demo #3 explains that if the mean is lower than the median, the distribution is negatively skewed.
I developed Design vs. Data for a guest lecture on quantitative research. I decided to focus on the importance of starting with an important question and the value of good design over data collection and statistics.
A study surveyed 300 breast cancer patients taking the drug Capvex. 250 of the patients were healed after 10 weeks. The parameter is the proportion of all patients taking Capvex who are healed within 10 weeks. The statistic is the proportion of 250 out of the 300 patients in the sample who were healed after 10 weeks.
This document discusses goodness of fit, which determines how close an observed pattern fits a hypothesized pattern. It provides examples comparing hypothesized and actual counts or percentages to determine if they are statistically significantly different. If comparing counts, a goodness of fit test is used, while a difference test is used for percentages. The document explains this using examples of testing the distribution of M&M colors and rates of student absenteeism.
Infernetial vs desctiptive (jejit + indepth)Ken Plummer
The document discusses the differences between descriptive and inferential statistics. Descriptive statistics are used to describe characteristics of a whole population, while inferential statistics are used when the whole population cannot be measured and conclusions are drawn from a sample to make generalizations about the larger population. Examples are provided to illustrate when each type of statistic would be used.
Exercise 7-1 Q # 10 Number of faculty. the numbers of faculty .docxgitagrimston
Exercise 7-1
Q # 10
Number of faculty. the numbers of faculty at 32 randomly selected state-controlled colleges and universities with enrollment under 12,000 students are shown below. use these data to estimate the mean number of faculty at all state-controlled colleges and universities with enrollment under 12,000 with 92% confidence. assume .
211
384
396
211
224
337
395
121
356
621
367
408
515
280
289
180
431
176
318
836
203
374
224
121
412
134
539
471
638
425
159
324
Q # 14
Number of jobs. a sociologist found that in a sample of 50 retired men, the average number of jobs they had during their lifetimes was 7.2. the population standard deviation is 2.1.
a. find the best point to estimate of the population men.
b.find the 95 % confidence interval of the mean number of jobs.
c.find the 99% confidence interval of the mean number of jobs.
d. which is smaller? explain why.
Q # 18
Day care tuition. a random sample of 50 four-year-olds attending day care centers provided a yearly tuition average of $3987 and the population standard deviation of $630. find the 90% confidence interval of the true mean. if a day care center were starting up and wanted to keep tuition low. what would be a reasonable amount to charge?
Exercise 7-2
Q # 8
State Gasoline Taxes. a random sample of state gasoline taxes ( in cents ) is shown here for 12 states. use the data to estimate the true population mean gasoline tax with 90% confidence. does your interval contain the national average of 44.7 cents?
38.4
40.9
67
32.5
51.5
43.4
38
43.4
50.7
35.4
39.3
41.4
Q # 10
Dance Company Students. the number of students who belong to dance company at each of several randomly selected small universities is shown below. estimate the true population mean size of a university dance company with 99% confidence.
21
25
32
22
28
30
29
30
47
26
35
26
35
26
28
28
32
27
40
Exercise 7-3
Q # 6
Belief in haunted places. a random sample of 205 college students were asked if they believed that places could be haunted, and 65 responded yes. estimate the true proportion of college students who believed in the possibility of haunted places with 99% confidence. according to time magazine,37% of americans believe that places can be haunted.
Q # 14
Fighting U.S hunger. in a poll of 1000 likely voters, 560 say that the united states spends too little on fighting hunger at home. find a 95% confidence interval for the true proportion of voters who feel this way.
Exercise 8-2
Q # 4
Moviegoers. the average moviegoer sees 8.5 movies a year. a moviegoer is defined as a person who sees at least one movie in a theater in a 12 month period. a random sample of 40 moviegoers from a large university revealed that the average number of movies seen per person was 9.6. The population standard deviation is 3.2 movies. at the 0.05 level of significance, can it be concluded that this represents a difference from the national average?
Q # 8
Salaries of government employees. the mean salary o ...
Quantitative analysis in language researchCarlo Magno
Here are the analyses for each case:
Case A: Cross-tabulation and chi-square test since it involves counting students in categories defined by gender and track preference (both nominal variables).
Case B: One-way ANOVA since it involves comparing the mean attitude scores of 3 ethnic groups (nominal IV with 3 levels) on the Likert scale questionnaire (interval DV).
Case C: Independent t-test since it involves comparing the mean English exposure scores of 2 groups defined by parents' English proficiency (nominal IV with 2 levels) on the interval scale questionnaire.
Case D: Mann-Whitney U test since it involves comparing the rankings of 2 groups defined by where they studied (nominal IV)
The document discusses research design and statistical concepts for evaluating library statistics. It covers topics like validity, reliability, generalizability, research questions, hypotheses, data definitions, sampling, data collection, scales of measurement, distributions, variables, and statistical tests. Examples of case studies analyzing citation analysis, usage analysis and service analysis in libraries are provided to demonstrate key concepts.
The sampling methods used in each situation are:
1. Cluster sampling (schools were randomly selected and then all students within those schools were included)
2. Simple random sampling (each child had an equal probability of being selected using a random number assignment)
3. Stratified random sampling (the population was divided into subgroups/strata and then a random sample was selected from each)
4. Cluster sampling (sections/clusters were randomly selected and then all students within those sections were included)
The document provides guidance on reporting the results of a single sample t-test in APA format. It includes an example result that states there was no statistically significant difference in calculus anxiety scores between a sample of 30 students and the general college student population based on a t-value of 1.03 and p-value of 0.434. Key elements to report include the sample mean and standard deviation, degrees of freedom, t-value, and p-value.
This document provides an introduction to statistical theory. It discusses why statistics are studied and defines key statistical concepts such as populations, samples, parameters, statistics, descriptive statistics, inferential statistics, and the different types of data and variables. It also covers experimental design, methods for collecting data such as surveys and sampling, and different sampling methods like random, stratified, cluster, and systematic sampling.
35878 Topic Discussion5Number of Pages 1 (Double Spaced).docxrhetttrevannion
35878 Topic: Discussion5
Number of Pages: 1 (Double Spaced)
Number of sources: 1
Writing Style: APA
Type of document: Essay
Academic Level:Master
Category: Psychology
Language Style: English (U.S.)
Order Instructions: Attached
I will attach the instruction
Please follow them carefully
General Business Page 9
Unit 4
Due Wed 12/12
800-1,000 words / these will be turned into slides and added to your key assignment.
Study the following document: Methods for Managing Differences. Assume this communication strategy has been recommended by your employer for mediation when working with potential and existing business clients and partners.
Consider that there are basically two distinct types of cultures. One type is more cooperative, and the other is more competitive. It has been discovered that there are some conflicts occurring between some of the key players who need to come to agreement on specific critical areas of the deal for it to move forward. The top management would really like this deal to happen.
Imagine being in this situation, and create the scenario as you go through the process using the methods approach from above.
· Describe the steps you would take and any considerations along the way.
· How would you use the recommended method when working with individuals who exhibit a generally competitive culture?
· How would you use the recommended method when working with individuals who exhibit a generally cooperative culture?
· Would this cultural factor change the way you apply this method for managing differences? Why or why not? Explain.
Create Section 4 of your Key Assignment presentation: Global Negotiations. Refer to Unit 1 Discussion Board 2 for a description of this section. Submit a draft of your entire presentation for your instructor to review.
Discussion 2: Discuss, elaborate and give example on the topic below. Please use only the reference I attach. Please be careful with grammar and spelling. No running head Please.
Author: Jackson, S.L. (2017). Statistics Plain and Simple (4th ed.): Cengage Learning
Topic
Review this week’s course materials and learning activities, and reflect on your learning so far this week. Respond to one or more of the following prompts in one to two paragraphs:
1. Provide citation and reference to the material(s) you discuss. Describe what you found interesting regarding this topic, and why.
2. Describe how you will apply that learning in your daily life, including your work life.
3. Describe what may be unclear to you, and what you would like to learn.
Reference:
Module 9: The Single-Sample z Test
The z Test: What It Is and What It Does
The Sampling Distribution
The Standard Error of the Mean
Calculations for the One-Tailed z Test
Interpreting the One-Tailed z Test
Calculations for the Two-Tailed z Test
Interpreting the Two-Tailed z Test
Statistical Power
Assumptions and Appropriate Use of the z Test
Confidence Intervals Based on the z Distribution
Review of Key Term.
Standardized testing can take two forms: norm-referenced which compares test takers to each other, and criterion-referenced which determines if an individual has achieved a specified standard. Norm-referenced testing aims to discriminate between test takers in order to distribute scarce resources like university places. It became popular during WWI when psychological testing was used to contribute to the war effort. Proponents viewed testing as a scientific process of quantifying and measuring abilities. However, others argue that defining and measuring constructs like traits is problematic. Test scores are distributed along a normal curve and take on meaning based on their position within that distribution compared to other test takers. Reliability ensures test scores are consistent over time without instruction.
Similar to Are the samples repeated or independent (20)
Diff rel gof-fit - jejit - practice (5)Ken Plummer
The document discusses the differences between questions of difference, relationship, and goodness of fit. It provides examples to illustrate each type of question. A question of difference compares two or more groups on some outcome, like comparing younger and older drivers' average driving speeds. A question of relationship examines whether a change in one variable causes a change in another, such as the relationship between age and flexibility. A question of goodness of fit assesses how well a claim matches reality, such as whether a salesman's claim of software effectiveness fits the results of user testing.
This document provides examples of questions that ask for the lowest and highest number in a set of data. The questions ask for the difference between the state with the lowest and highest church attendance, the students with the highest and lowest test scores, and the slowest and fastest versions of a vehicle model.
Inferential vs descriptive tutorial of when to use - Copyright UpdatedKen Plummer
The document discusses the differences between descriptive and inferential statistics. Descriptive statistics are used to describe characteristics of a whole population, while inferential statistics are used when the whole population cannot be measured and conclusions are drawn from a sample to generalize to the larger population. Examples are provided to illustrate when each type of statistic would be used. Key differences include descriptive statistics examining entire populations while inferential statistics examine samples that aim to infer conclusions about populations.
Diff rel ind-fit practice - Copyright UpdatedKen Plummer
The document provides explanations and examples for different types of statistical questions:
- Difference questions compare two or more groups on an outcome.
- Relationship questions examine if a change in one variable is associated with a change in another variable.
- Independence questions determine if two variables with multiple levels are independent of each other.
- Goodness of fit questions assess how well a claim matches reality.
Examples are given for each type of question to illustrate key concepts like comparing groups, examining associations between variables, assessing independence, and evaluating how a claim fits observed data.
Normal or skewed distributions (inferential) - Copyright updatedKen Plummer
- The document discusses determining whether distributions are normal or skewed
- A distribution is considered skewed if the skewness value divided by the standard error of skewness is less than -2 or greater than 2
- For the old car data set in the example, the skewness value of -4.26 divided by the standard error is less than -2, so this distribution is negatively skewed
- The new car data set skewness value of -1.69 divided by the standard error is between -2 and 2, so this distribution is normal
Normal or skewed distributions (descriptive both2) - Copyright updatedKen Plummer
The document discusses normal and skewed distributions and how to identify them. It provides examples of measuring forearm circumference of golf players and IQs of cats and dogs. The forearm circumference data is normally distributed while the dog IQ data is left skewed based on the skewness statistics provided. Therefore, at least one of the distributions (dog IQs) is skewed.
Nature of the data practice - Copyright updatedKen Plummer
The document discusses different types of data:
- Scaled data provides exact amounts like 12.5 feet or 140 miles per hour.
- Ordinal or ranked data provides comparative amounts like 1st, 2nd, 3rd place.
- Nominal data names or categorizes values like Republican or Democrat.
- Nominal proportional data are simply percentages like Republican 45% or Democrat 55%.
Nature of the data (spread) - Copyright updatedKen Plummer
The document discusses scaled and ordinal data. Scaled data can be measured in exact amounts like distances and speeds. Ordinal data provides comparative amounts by ranking items, like the top 3 states in terms of well-being. Examples ask the reader to identify if data is scaled or ordinal, like driving speeds which are scaled, or baby weight percentiles which are ordinal as they compare weights.
The document is a series of questions and examples that explain what it means for a question to ask about the "most frequent response". It provides examples of questions asking about the highest/most number of something based on data in tables or lists. It then asks a series of questions to determine if they are asking about the most frequent/common response based on the data given.
Nature of the data (descriptive) - Copyright updatedKen Plummer
The document discusses two types of data: scaled data and ordinal data. Scaled data can be measured in exact amounts with equal intervals between values. Ordinal or ranked data provides comparative amounts but not necessarily equal intervals. Several examples are provided to illustrate the difference, including driving speed, states ranked by well-being, and elephant weights. Practice questions are also included for the reader to determine if data examples provided are scaled or ordinal.
The document discusses whether variables are dichotomous or scaled when calculating correlations. It provides examples of correlations between ACT scores and whether students attended private or public school. One example has ACT scores as a scaled variable and school type as dichotomous. Another has lower and higher ACT scores as dichotomous and school type as dichotomous. It emphasizes determining if variables are both dichotomous, or if one is dichotomous and one is scaled.
The document discusses the correlation between ACT scores and a measure of school belongingness. It determines that one of the variables, which has a sample size less than 30, is skewed and has many ties. As a result, a non-parametric test should be used to analyze the relationship between the two variables.
The document discusses using parametric versus non-parametric tests based on sample size for skewed distributions. For skewed distributions with a sample size less than 30, a non-parametric test is recommended. For skewed distributions with a sample size greater than or equal to 30, a parametric test is recommended. It provides examples analyzing the correlation between ACT scores and sense of school belongingness using both approaches.
The document discusses whether there are many ties or few/no ties within the variables of the relationship question "What is the correlation between ACT rankings (ordinal) and sense of school belongingness (scaled 1-10)?". It determines that ACT rankings, being ordinal, have many ties, while sense of school belongingness, being on a scale of 1-10, may have many or few ties depending on how scores are distributed.
The document discusses identifying whether variables in statistical analyses are ordinal or nominal. It provides examples of relationships between variables such as ACT rankings and sense of school belongingness, daily social media use and sense of well-being, and private/public school enrollment and sense of well-being. It asks the reader to identify if variables in examples like running speed and shoe/foot size or LSAT scores and test anxiety are ordinal or nominal.
The document discusses covariates and their impact on relationships between variables. It defines a covariate as a variable that is controlled for or eliminated from a study. It explains that if a covariate is related to one of the variables in the relationship being examined, it can impact the strength of that relationship. Examples are provided to demonstrate when a question involves a covariate or not.
This document discusses the nature of variables in relationship questions. It can be determined that the variables are either both scaled, at least one is ordinal, or at least one is nominal. Examples of different relationship questions are provided that fall into each of these categories. The document also provides practice questions for the user to determine which category the variables fall into.
The document discusses the number of variables involved in research questions. It explains that many relationship questions deal with two variables, such as gender predicting driving speed. However, some questions deal with three or more variables, for example gender and age predicting driving speed. The document asks the reader to identify whether example research questions involve two or three or more variables.
The document discusses independent and dependent variables in research questions. It provides examples to illustrate that an independent variable has at least two levels and may have more, such as religious affiliation having two levels (Western religion and Eastern religion) or company type having three levels (Company X, Company Y, Company Z). It then provides a practice example about employee satisfaction rates among morning, afternoon, and evening shifts, identifying shift status as the independent variable with three levels.
The document discusses independent variables and how they relate to research questions. It provides examples of questions with one independent variable, two independent variables, and zero independent variables. An independent variable influences or impacts a dependent variable. Questions are presented about employee satisfaction rates, agent commissions, training proficiency, and cyberbullying incidents to illustrate different numbers of independent variables.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Thinking of getting a dog? Be aware that breeds like Pit Bulls, Rottweilers, and German Shepherds can be loyal and dangerous. Proper training and socialization are crucial to preventing aggressive behaviors. Ensure safety by understanding their needs and always supervising interactions. Stay safe, and enjoy your furry friends!
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
8. You have been asked to determine if ACT scores from
Texas students are similar to national student ACT scores.
9. You have been asked to determine if ACT scores from
Texas students are similar to national student ACT scores.
You select a sample of 100 student ACT scores from Texas
and determine if they are statistically similar to national
ACT scores.
13. Here is the problem again:
You have been asked to determine if ACT scores from
Texas students are similar to national student ACT
scores. You select a sample of 100 student ACT scores
from Texas and determine if they are statistically similar
to national ACT scores.
14. Here is the problem again:
You have been asked to determine if ACT scores from
Texas students are similar to national student ACT
scores. You select a sample of 100 student ACT scores
from Texas and determine if they are statistically similar
to national ACT scores.
What are the numeric values in this problem?
15. Here is the problem again:
You have been asked to determine if ACT scores from
Texas students are similar to national student ACT
scores. You select a sample of 100 student ACT scores
from Texas and determine if they are statistically similar
to national ACT scores.
What are the numeric values in this problem?
ACT scores
16. Here is the problem again:
You have been asked to determine if ACT scores from
Texas students are similar to national student ACT
scores. You select a sample of 100 student ACT scores
from Texas and determine if they are statistically similar
to national ACT scores.
What group produced these scores?
17. Here is the problem again:
You have been asked to determine if ACT scores from
Texas students are similar to national student ACT
scores. You select a sample of 100 student ACT scores
from Texas and determine if they are statistically similar
to national ACT scores.
What group produced these scores?
Texas Students
18. Here is the problem again:
You have been asked to determine if ACT scores from
Texas students are similar to national student ACT
scores. You select a sample of 100 student ACT scores
from Texas and determine if they are statistically similar
to national ACT scores.
What is the basis for group membership?
19. Here is the problem again:
You have been asked to determine if ACT scores from
Texas students are similar to national student ACT
scores. You select a sample of 100 student ACT scores
from Texas and determine if they are statistically similar
to national ACT scores.
What is the basis for group membership?
Being a student from Texas who took the ACT
21. Here is what that sample might look like:
100 Texas
Student ACT
Scores
22. Here is what that sample might look like:
100 Texas
Student ACT
Scores
Data Set
23. Here is what that sample might look like:
100 Texas
Student ACT
Scores
Texas
Students
ACT
Scores
1 25
2 16
3 28
4 31
5 14
. . .
. . .
100 32
Data Set
24. 100 Texas
Student ACT
Scores
Texas
Students
ACT
Scores
1 25
2 16
3 28
4 31
5 14
. . .
. . .
100 32
Data Set
Back to the definition:
25. A sample is list of numeric values produced by a
group of individuals or from observations that have
some common characteristic.
100 Texas
Student ACT
Scores
Texas
Students
ACT
Scores
1 25
2 16
3 28
4 31
5 14
. . .
. . .
100 32
Data Set
26. A sample is list of numeric values produced by a
group of individuals or from observations that have
some common characteristic.
100 Texas
Student ACT
Scores
Texas
Students
ACT
Scores
1 25
2 16
3 28
4 31
5 14
. . .
. . .
100 32
Data Set
27. A sample is list of numeric values produced by a
group of individuals or from observations that have
some common characteristic.
100 Texas
Student ACT
Scores
Texas
Students
ACT
Scores
1 25
2 16
3 28
4 31
5 14
. . .
. . .
100 32
Data Set
28. A sample is list of numeric values produced by a
group of individuals or from observations that have
some common characteristic.
100 Texas
Student ACT
Scores
Texas
Students
ACT
Scores
1 25
2 16
3 28
4 31
5 14
. . .
. . .
100 32
Data Set
29. A sample is list of numeric values produced by a
group of individuals or from observations that have
some common characteristic.
100 Texas
Student ACT
Scores
Texas
Students
ACT
Scores
1 25
2 16
3 28
4 31
5 14
. . .
. . .
100 32
Data Set
30. A sample is list of numeric values produced by a
group of individuals or from observations that have
some common characteristic.
100 Texas
Student ACT
Scores
Texas
Students
ACT
Scores
1 25
2 16
3 28
4 31
5 14
. . .
. . .
100 32
Data Set
32. Now that you’ve been introduced to what sample is
. . . What are Independent Samples?
33. A sample is independent from another sample when
the subjects or observations in one sample have NO
RELATIONSHIP with the subjects or observations in
another sample.
35. Imagine you have been asked to compare ACT scores
between Texas and California students.
36. What makes these samples independent
is that these Texas Students ARE NOT
these California Students
37. What makes these samples independent
is that these Texas Students ARE NOT
these California Students
100 Texas
Student ACT
Scores
38. What makes these samples independent
is that these Texas Students ARE NOT
these California Students
100 Texas
Student ACT
Scores
39. What makes these samples independent
is that these Texas Students ARE NOT
these California Students
100 Texas
Student ACT
Scores
100 California
Student ACT
Scores
40. This may seem very obvious that one groups
individuals are not the other groups individuals.
41. This may seem very obvious that one groups
individuals are not the other groups individuals. But,
it is an important aspect that makes independent
samples – independent!
43. An investigator thinks that people under the age of
forty have vocabularies that are different than those
of people over sixty years of age. The investigator
administers a vocabulary test to a group of 40 younger
subjects and to a group of 45 older subjects. Higher
scores reflect better performance. The mean score for
younger subjects was 14.0 and the mean score for
older subjects was 20.0.
44. An investigator thinks that people under the age of
forty have vocabularies that are different than those
of people over sixty years of age. The investigator
administers a vocabulary test to a group of 40 younger
subjects and to a group of 45 older subjects. Higher
scores reflect better performance. The mean score for
younger subjects was 14.0 and the mean score for
older subjects was 20.0.
45. An investigator thinks that people under the age of
forty have vocabularies that are different than those
of people over sixty years of age. The investigator
administers a vocabulary test to a group of 40 younger
subjects and to a group of 45 older subjects. Higher
scores reflect better performance. The mean score for
younger subjects was 14.0 and the mean score for
older subjects was 20.0.
46. An investigator thinks that people under the age of
forty have vocabularies that are different than those
of people over sixty years of age. The investigator
administers a vocabulary test to a group of 40 younger
subjects and to a group of 45 older subjects. Higher
scores reflect better performance. The mean score for
younger subjects was 14.0 and the mean score for
older subjects was 20.0.
How many samples are there?
47. An investigator thinks that people under the age of
forty have vocabularies that are different than those
of people over sixty years of age. The investigator
administers a vocabulary test to a group of 40 younger
subjects and to a group of 45 older subjects. Higher
scores reflect better performance. The mean score for
younger subjects was 14.0 and the mean score for
older subjects was 20.0.
Sample 1
How many samples are there?
48. An investigator thinks that people under the age of
forty have vocabularies that are different than those
of people over sixty years of age. The investigator
administers a vocabulary test to a group of 40 younger
subjects and to a group of 45 older subjects. Higher
scores reflect better performance. The mean score for
younger subjects was 14.0 and the mean score for
older subjects was 20.0.
Sample 2
How many samples are there?
49. An investigator thinks that people under the age of
forty have vocabularies that are different than those
of people over sixty years of age. The investigator
administers a vocabulary test to a group of 40 younger
subjects and to a group of 45 older subjects. Higher
scores reflect better performance. The mean score for
younger subjects was 14.0 and the mean score for
older subjects was 20.0.
Are they independent?
55. With repeated samples the two samples share one
important thing in common: They are the SAME
PERSONS being measured . . .
56. With repeated samples the two samples share one
important thing in common: They are the SAME
PERSONS being measured more than once . . .
57. With repeated samples the two samples share one
important thing in common: They are the SAME
PERSONS being measured more than once or they are
different persons but MATCHED in some way.
59. Suppose that, as a health researcher, you want to
examine the impact of a specialized dietary regimen
on hours of sleep.
60. Suppose that, as a health researcher, you want to
examine the impact of a specialized dietary regimen
on hours of sleep. Before they start the regimen, you
measure 45 subject’s average sleep hours.
61. Suppose that, as a health researcher, you want to
examine the impact of a specialized dietary regimen
on hours of sleep. Before they start the regimen, you
measure 45 subject’s average sleep hours. One month
later you take their average number of sleep hours
again.
62. Suppose that, as a health researcher, you want to
examine the impact of a specialized dietary regimen
on hours of sleep. Before they start the regimen, you
measure 45 subject’s average sleep hours. One month
later you take their average number of sleep hours
again. And then two months after that you take the
measure one more time.
63. Suppose that, as a health researcher, you want to
examine the impact of a specialized dietary regimen
on hours of sleep. Before they start the regimen, you
measure 45 subject’s average sleep hours. One month
later you take their average number of sleep hours
again. And then two months after that you take the
measure one more time.
You will notice that there is only one group we are studying
64. Suppose that, as a health researcher, you want to
examine the impact of a specialized dietary regimen
on hours of sleep. Before they start the regimen, you
measure 45 subject’s average sleep hours. One month
later you take their average number of sleep hours
again. And then two months after that you take the
measure one more time.
You will notice that there is only one group we are studying
65. Suppose that, as a health researcher, you want to
examine the impact of a specialized dietary regimen
on hours of sleep. Before they start the regimen, you
measure 45 subject’s average sleep hours. One month
later you take their average number of sleep hours
again. And then two months after that you take the
measure one more time.
66. Suppose that, as a health researcher, you want to
examine the impact of a specialized dietary regimen
on hours of sleep. Before they start the regimen, you
measure 45 subject’s average sleep hours. One month
later you take their average number of sleep hours
again. And then two months after that you take the
measure one more time.
Subjects
Subject 1
Subject 2
. . .
Subject 45
67. Suppose that, as a health researcher, you want to
examine the impact of a specialized dietary regimen
on hours of sleep. Before they start the regimen, you
measure 45 subject’s average sleep hours. One month
later you take their average number of sleep hours
again. And then two months after that you take the
measure one more time.
Subjects
Subject 1
Subject 2
. . .
Subject 45
68. Suppose that, as a health researcher, you want to
examine the impact of a specialized dietary regimen
on hours of sleep. Before they start the regimen, you
measure 45 subject’s average sleep hours. One month
later you take their average number of sleep hours
again. And then two months after that you take the
measure one more time.
Before the
Study
Subjects
Subject 1
Subject 2
. . .
Subject 45
69. Suppose that, as a health researcher, you want to
examine the impact of a specialized dietary regimen
on hours of sleep. Before they start the regimen, you
measure 45 subject’s average sleep hours. One month
later you take their average number of sleep hours
again. And then two months after that you take the
measure one more time.
Before the
Study
Subjects Hours of
Sleep
Subject 1 5
Subject 2 4
. . .
Subject 45 7
70. Suppose that, as a health researcher, you want to
examine the impact of a specialized dietary regimen
on hours of sleep. Before they start the regimen, you
measure 45 subject’s average sleep hours. One month
later you take their average number of sleep hours
again. And then two months after that you take the
measure one more time.
Before the
Study
Subjects Hours of
Sleep
Subject 1 5
Subject 2 4
. . .
Subject 45 7
71. Suppose that, as a health researcher, you want to
examine the impact of a specialized dietary regimen
on hours of sleep. Before they start the regimen, you
measure 45 subject’s average sleep hours. One month
later you take their average number of sleep hours
again. And then two months after that you take the
measure one more time.
Before the
Study
Subjects Hours of
Sleep
Subject 1 5
Subject 2 4
. . .
Subject 45 7
One Month
Later
Hours of
Sleep
6
5
8
72. Suppose that, as a health researcher, you want to
examine the impact of a specialized dietary regimen
on hours of sleep. Before they start the regimen, you
measure 45 subject’s average sleep hours. One month
later you take their average number of sleep hours
again. And then two months after that you take the
measure one more time.
Before the
Study
Subjects Hours of
Sleep
Subject 1 5
Subject 2 4
. . .
Subject 45 7
One Month
Later
Hours of
Sleep
6
5
8
73. Suppose that, as a health researcher, you want to
examine the impact of a specialized dietary regimen
on hours of sleep. Before they start the regimen, you
measure 45 subject’s average sleep hours. One month
later you take their average number of sleep hours
again. And then two months after that you take the
measure one more time.
Before the
Study
Subjects Hours of
Hours of Sleep
6
5
8
Sleep
Subject 1 5
Subject 2 4
. . .
Subject 45 7
One Month
Later
Hours of
Sleep
6
5
8
Two Months
Later
Hours of
Sleep
7
6
8
74. Suppose that, as a health researcher, you want to
examine the impact of a specialized dietary regimen
on hours of sleep. Before they start the regimen, you
measure 45 subject’s average sleep hours. One month
later you take their average number of sleep hours
again. And then two months after that you take the
measure one more time.
Notice that the research subjects are
the same, but the samples are taken
at different times.
Before the
Study
Subjects Hours of
Hours of Sleep
6
5
8
Sleep
Subject 1 5
Subject 2 4
. . .
Subject 45 7
One Month
Later
Hours of
Sleep
6
5
8
Two Months
Later
Hours of
Sleep
7
6
8
75. Suppose that, as a health researcher, you want to
examine the impact of a specialized dietary regimen
on hours of sleep. Before they start the regimen, you
measure 45 subject’s average sleep hours. One month
later you take their average number of sleep hours
again. And then two months after that you take the
measure one more time.
Notice that the research subjects are
the same, but the samples are taken
at different times.
Before the
Study
Subjects Hours of
Hours of Sleep
6
5
8
Sleep
Subject 1 5
Subject 2 4
. . .
Subject 45 7
One Month
Later
Hours of
Sleep
6
5
8
Two Months
Later
Hours of
Sleep
7
6
8
76. Suppose that, as a health researcher, you want to
examine the impact of a specialized dietary regimen
on hours of sleep. Before they start the regimen, you
measure 45 subject’s average sleep hours. One month
later you take their average number of sleep hours
again. And then two months after that you take the
measure one more time.
Notice that the research subjects are
the same, but the samples are taken
at different times.
Before the
Study
Subjects Hours of
Hours of Sleep
6
5
8
Sleep
Subject 1 5
Subject 2 4
. . .
Subject 45 7
One Month
Later
Hours of
Sleep
6
5
8
Two Months
Later
Hours of
Sleep
7
6
8
77. Suppose that, as a health researcher, you want to
examine the impact of a specialized dietary regimen
on hours of sleep. Before they start the regimen, you
measure 45 subject’s average sleep hours. One month
later you take their average number of sleep hours
again. And then two months after that you take the
measure one more time.
Notice that the research subjects are
the same, but the samples are taken
at different times.
Before the
Study
Subjects Hours of
Hours of Sleep
6
5
8
Sleep
Subject 1 5
Subject 2 4
. . .
Subject 45 7
One Month
Later
Hours of
Sleep
6
5
8
Two Months
Later
Hours of
Sleep
7
6
8
78. Suppose that, as a health researcher, you want to
examine the impact of a specialized dietary regimen
on hours of sleep. Before they start the regimen, you
measure 45 subject’s average sleep hours. One month
later you take their average number of sleep hours
again. And then two months after that you take the
measure one more time.
Notice that the research subjects are
the same, but the samples are taken
at different times.
Before the
Study
Subjects Hours of
Hours of Sleep
6
5
8
Sleep
Subject 1 5
Subject 2 4
. . .
Subject 45 7
One Month
Later
Hours of
Sleep
6
5
8
Two Months
Later
Hours of
Sleep
7
6
8
79. Suppose that, as a health researcher, you want to
examine the impact of a specialized dietary regimen
on hours of sleep. Before they start the regimen, you
measure 45 subject’s average sleep hours. One month
later you take their average number of sleep hours
again. And then two months after that you take the
measure one more time.
Notice that the research subjects are
the same, but the samples are taken
at different times.
Before the
Study
Subjects Hours of
Hours of Sleep
6
5
8
Sleep
Subject 1 5
Subject 2 4
. . .
Subject 45 7
One Month
Later
Hours of
Sleep
6
5
8
Two Months
Later
Hours of
Sleep
7
6
8
80. These samples are repeated because in this case each
sample has the same person in it being measured
repeatedly.
81. In some instances, the persons are not the same but
are matched on some variable.
82. In some instances, the persons are not the same but
are matched on some variable.
In such a scenario, the samples would be considered
to be repeated.
84. June Hours
of sleep
July Hours of
sleep
August Hours of
sleep
Males from Minnesota
over 65 with heart
disease
Bob 5 Tanner 6 Mckay 5
Males from California
over 65 without heart
disease
Ashton 4 Roger 3 Steve 4
Females from Utah under
65 with heart disease
Keaton 5 Kris 6 Phil 6
Males from Texas under
65 with lung disease
Lynn 7 Ed 8 Kade 8
85. June Hours
of sleep
July Hours of
sleep
August Hours of
sleep
Males from Minnesota
over 65 with heart
disease
Bob 5 Tanner 6 Mckay 5
Males from California
over 65 without heart
disease
Ashton 4 Roger 3 Steve 4
Females from Utah under
65 with heart disease
Keaton 5 Kris 6 Phil 6
Males from Texas under
65 with lung disease
Lynn 7 Ed 8 Kade 8
First, notice that there are multiple
measurements over time.
86. June Hours
of sleep
July Hours of
sleep
August Hours of
sleep
Males from Minnesota
over 65 with heart
disease
Bob 5 Tanner 6 Mckay 5
Males from California
over 65 without heart
disease
Ashton 4 Roger 3 Steve 4
Females from Utah under
65 with heart disease
Keaton 5 Kris 6 Phil 6
Males from Texas under
65 with lung disease
Lynn 7 Ed 8 Kade 8
First, notice that there are multiple
measurements over time.
87. June Hours
of sleep
July Hours of
sleep
August Hours of
sleep
Males from Minnesota
over 65 with heart
disease
Bob 5 Tanner 6 Mckay 5
Males from California
over 65 without heart
disease
Ashton 4 Roger 3 Steve 4
Females from Utah under
65 with heart disease
Keaton 5 Kris 6 Phil 6
Males from Texas under
65 with lung disease
Lynn 7 Ed 8 Kade 8
First, notice that there are multiple
measurements over time.
88. June Hours
of sleep
July Hours of
sleep
August Hours of
sleep
Males from Minnesota
over 65 with heart
disease
Bob 5 Tanner 6 Mckay 5
Males from California
over 65 without heart
disease
Ashton 4 Roger 3 Steve 4
Females from Utah under
65 with heart disease
Keaton 5 Kris 6 Phil 6
Males from Texas under
65 with lung disease
Lynn 7 Ed 8 Kade 8
First, notice that there are multiple
measurements over time.
89. June Hours
of sleep
July Hours of
sleep
August Hours of
sleep
Males from Minnesota
over 65 with heart
disease
Bob 5 Tanner 6 Mckay 5
Males from California
over 65 without heart
disease
Ashton 4 Roger 3 Steve 4
Females from Utah under
65 with heart disease
Keaton 5 Kris 6 Phil 6
Males from Texas under
65 with lung disease
Lynn 7 Ed 8 Kade 8
Next notice that Bob, Tanner, and Mckay are all
matched on four variables.
90. June Hours
of sleep
July Hours of
sleep
August Hours of
sleep
Males from Minnesota
over 65 with heart
disease
Bob 5 Tanner 6 Mckay 5
Males from California
over 65 without heart
disease
Ashton 4 Roger 3 Steve 4
Females from Utah under
65 with heart disease
Keaton 5 Kris 6 Phil 6
Males from Texas under
65 with lung disease
Lynn 7 Ed 8 Kade 8
Next notice that Bob, Tanner, and Mckay are all
matched on four variables.
91. June Hours
of sleep
July Hours of
sleep
August Hours of
sleep
Males from Minnesota
over 65 with heart
disease
Bob 5 Tanner 6 Mckay 5
Males from California
over 65 without heart
disease
Ashton 4 Roger 3 Steve 4
Females from Utah under
65 with heart disease
Keaton 5 Kris 6 Phil 6
Males from Texas under
65 with lung disease
Lynn 7 Ed 8 Kade 8
Next notice that Bob, Tanner, and Mckay are all
matched on four variables.
92. June Hours
of sleep
July Hours of
sleep
August Hours of
sleep
Males from Minnesota
over 65 with heart
disease
Bob 5 Tanner 6 Mckay 5
Males from California
over 65 without heart
disease
Ashton 4 Roger 3 Steve 4
Females from Utah under
65 with heart disease
Keaton 5 Kris 6 Phil 6
Males from Texas under
65 with lung disease
Lynn 7 Ed 8 Kade 8
Next notice that Bob, Tanner, and Mckay are all
matched on four variables.
93. June Hours
of sleep
July Hours of
sleep
August Hours of
sleep
Males from Minnesota
over 65 with heart
disease
Bob 5 Tanner 6 Mckay 5
Males from California
over 65 without heart
disease
Ashton 4 Roger 3 Steve 4
Females from Utah under
65 with heart disease
Keaton 5 Kris 6 Phil 6
Males from Texas under
65 with lung disease
Lynn 7 Ed 8 Kade 8
Next notice that Bob, Tanner, and Mckay are all
matched on four variables.
94. June Hours
of sleep
July Hours of
sleep
August Hours of
sleep
Males from Minnesota
over 65 with heart
disease
Bob 5 Tanner 6 Mckay 5
Males from California
over 65 without heart
disease
Ashton 4 Roger 3 Steve 4
Females from Utah under
65 with heart disease
Keaton 5 Kris 6 Phil 6
Males from Texas under
65 with lung disease
Lynn 7 Ed 8 Kade 8
Next notice that Bob, Tanner, and Mckay are all
matched on four variables.
95. June Hours
of sleep
July Hours of
sleep
August Hours of
sleep
Males from Minnesota
over 65 with heart
disease
Bob 5 Tanner 6 Mckay 5
Males from California
over 65 without heart
disease
Ashton 4 Roger 3 Steve 4
Females from Utah under
65 with heart disease
Keaton 5 Kris 6 Phil 6
Males from Texas under
65 with lung disease
1- Gender
Lynn 7 Ed 8 Kade 8
Next notice that Bob, Tanner, and Mckay are all
matched on four variables.
96. June Hours
of sleep
July Hours of
sleep
August Hours of
sleep
Males from Minnesota
over 65 with heart
disease
Bob 5 Tanner 6 Mckay 5
Males from California
over 65 without heart
disease
Ashton 4 Roger 3 Steve 4
Females from Utah under
65 with heart disease
Keaton 5 Kris 6 Phil 6
Males from Texas under
65 with lung disease
2- Residence
Lynn 7 Ed 8 Kade 8
Next notice that Bob, Tanner, and Mckay are all
matched on four variables.
97. June Hours
of sleep
July Hours of
sleep
August Hours of
sleep
Males from Minnesota
over 65 with heart
disease
Bob 5 Tanner 6 Mckay 5
Males from California
over 65 without heart
disease
Ashton 4 Roger 3 Steve 4
Females from Utah under
65 with heart disease
Keaton 5 Kris 6 Phil 6
Males from Texas under
65 with lung disease
3 - Age
Lynn 7 Ed 8 Kade 8
Next notice that Bob, Tanner, and Mckay are all
matched on four variables.
98. June Hours
of sleep
July Hours of
sleep
August Hours of
sleep
Males from Minnesota
over 65 with heart
disease
Bob 5 Tanner 6 Mckay 5
Males from California
over 65 without heart
disease
Ashton 4 Roger 3 Steve 4
Females from Utah under
65 with heart disease
Keaton 5 Kris 6 Phil 6
Males from Texas under
65 with lung disease
4- Heart
Condition
Lynn 7 Ed 8 Kade 8
Next notice that Bob, Tanner, and Mckay are all
matched on four variables.
99. June Hours
of sleep
July Hours of
sleep
August Hours of
sleep
Males from Minnesota
over 65 with heart
disease
Bob 5 Tanner 6 Mckay 5
Males from California
over 65 without heart
disease
Ashton 4 Roger 3 Steve 4
Females from Utah under
65 with heart disease
Keaton 5 Kris 6 Phil 6
Males from Texas under
65 with lung disease
Lynn 7 Ed 8 Kade 8
So, Bob, Tanner, and Mckay are not the same
person but they are matched in terms of gender,
residence, age and heart condition.
100. June Hours
of sleep
July Hours of
sleep
August Hours of
sleep
Males from Minnesota
over 65 with heart
disease
Bob 5 Tanner 6 Mckay 5
Males from California
over 65 without heart
disease
Ashton 4 Roger 3 Steve 4
Females from Utah under
65 with heart disease
Keaton 5 Kris 6 Phil 6
Males from Texas under
65 with lung disease
Lynn 7 Ed 8 Kade 8
So, Bob, Tanner, and Mckay are not the same
person but they are matched in terms of gender,
residence, age and heart condition.
101. June Hours
of sleep
July Hours of
sleep
August Hours of
sleep
Males from Minnesota
over 65 with heart
disease
Bob 5 Tanner 6 Mckay 5
Males from California
over 65 without heart
disease
Ashton 4 Roger 3 Steve 4
Females from Utah under
65 with heart disease
Keaton 5 Kris 6 Phil 6
Males from Texas under
65 with lung disease
Lynn 7 Ed 8 Kade 8
So, Bob, Tanner, and Mckay are not the same
person but they are matched in terms of gender,
residence, age and heart condition.
102. June Hours
of sleep
July Hours of
sleep
August Hours of
sleep
Males from Minnesota
over 65 with heart
disease
Bob 5 Tanner 6 Mckay 5
Males from California
over 65 without heart
disease
Ashton 4 Roger 3 Steve 4
Females from Utah under
65 with heart disease
Keaton 5 Kris 6 Phil 6
Males from Texas under
65 with lung disease
Lynn 7 Ed 8 Kade 8
So, Bob, Tanner, and Mckay are not the same
person but they are matched in terms of gender,
residence, age and heart condition.
103. June Hours
of sleep
July Hours of
sleep
August Hours of
sleep
Males from Minnesota
over 65 with heart
disease
Bob 5 Tanner 6 Mckay 5
Males from California
over 65 without heart
disease
Ashton 4 Roger 3 Steve 4
Females from Utah under
65 with heart disease
Keaton 5 Kris 6 Phil 6
Males from Texas under
65 with lung disease
Lynn 7 Ed 8 Kade 8
So, Bob, Tanner, and Mckay are not the same
person but they are matched in terms of gender,
residence, age and heart condition.
104. June Hours
of sleep
July Hours of
sleep
August Hours of
sleep
Males from Minnesota
over 65 with heart
disease
Bob 5 Tanner 6 Mckay 5
Males from California
over 65 without heart
disease
Ashton 4 Roger 3 Steve 4
Females from Utah under
65 with heart disease
Keaton 5 Kris 6 Phil 6
Males from Texas under
65 with lung disease
Lynn 7 Ed 8 Kade 8
So, Bob, Tanner, and Mckay are not the same
person but they are matched in terms of gender,
residence, age and heart condition.
105. June Hours
of sleep
July Hours of
sleep
August Hours of
sleep
Males from Minnesota
over 65 with heart
disease
Bob 5 Tanner 6 Mckay 5
Males from California
over 65 without heart
disease
Ashton 4 Roger 3 Steve 4
Females from Utah under
65 with heart disease
Keaton 5 Kris 6 Phil 6
Males from Texas under
65 with lung disease
Lynn 7 Ed 8 Kade 8
So, Bob, Tanner, and Mckay are not the same
person but they are matched in terms of gender,
residence, age and heart condition.
106. June Hours
of sleep
July Hours of
sleep
August Hours of
sleep
Males from Minnesota
over 65 with heart
disease
Bob 5 Tanner 6 Mckay 5
Males from California
over 65 without heart
disease
Ashton 4 Roger 3 Steve 4
Females from Utah under
65 with heart disease
Keaton 5 Kris 6 Phil 6
Males from Texas under
65 with lung disease
Lynn 7 Ed 8 Kade 8
So, Bob, Tanner, and Mckay are not the same
person but they are matched in terms of gender,
residence, age and heart condition.
107. June Hours
of sleep
July Hours of
sleep
August Hours of
sleep
Males from Minnesota
over 65 with heart
disease
Bob 5 Tanner 6 Mckay 5
Males from California
over 65 without heart
disease
Ashton 4 Roger 3 Steve 4
Females from Utah under
65 with heart disease
Keaton 5 Kris 6 Phil 6
Males from Texas under
65 with lung disease
Lynn 7 Ed 8 Kade 8
108. June Hours
of sleep
July Hours of
sleep
August Hours of
sleep
Males from Minnesota
over 65 with heart
disease
Bob 5 Tanner 6 Mckay 5
Males from California
over 65 without heart
disease
Ashton 4 Roger 3 Steve 4
Females from Utah under
65 with heart disease
Keaton 5 Kris 6 Phil 6
Males from Texas under
65 with lung disease
Lynn 7 Ed 8 Kade 8
The same is true for Ashton, Roger, and Steve
who are not the same person but who are also
matched in terms of gender, residence, age and
heart condition.
109. June Hours
of sleep
July Hours of
sleep
August Hours of
sleep
Males from Minnesota
over 65 with heart
disease
Bob 5 Tanner 6 Mckay 5
Males from California
over 65 without heart
disease
Ashton 4 Roger 3 Steve 4
Females from Utah under
65 with heart disease
Keaton 5 Kris 6 Phil 6
Males from Texas under
65 with lung disease
Lynn 7 Ed 8 Kade 8
The same is true for Ashton, Roger, and Steve
who are not the same person but who are also
matched in terms of gender, residence, age and
heart condition.
110. June Hours
of sleep
July Hours of
sleep
August Hours of
sleep
Males from Minnesota
over 65 with heart
disease
Bob 5 Tanner 6 Mckay 5
Males from California
over 65 without heart
disease
Ashton 4 Roger 3 Steve 4
Females from Utah under
65 with heart disease
Keaton 5 Kris 6 Phil 6
Males from Texas under
65 with lung disease
Lynn 7 Ed 8 Kade 8
111. June Hours
of sleep
July Hours of
sleep
August Hours of
sleep
Males from Minnesota
over 65 with heart
disease
Bob 5 Tanner 6 Mckay 5
Males from California
over 65 without heart
disease
Ashton 4 Roger 3 Steve 4
Females from Utah under
65 with heart disease
Keaton 5 Kris 6 Phil 6
Males from Texas under
65 with lung disease
Lynn 7 Ed 8 Kade 8
The same with Keaton, Kris, and Phil who are
also matched in terms of gender, residence, age
and heart condition.
112. June Hours
of sleep
July Hours of
sleep
August Hours of
sleep
Males from Minnesota
over 65 with heart
disease
Bob 5 Tanner 6 Mckay 5
Males from California
over 65 without heart
disease
Ashton 4 Roger 3 Steve 4
Females from Utah under
65 with heart disease
Keaton 5 Kris 6 Phil 6
Males from Texas under
65 with lung disease
Lynn 7 Ed 8 Kade 8
The same with Keaton, Kris, and Phil who are
also matched in terms of gender, residence, age
and heart condition.
113. June Hours
of sleep
July Hours of
sleep
August Hours of
sleep
Males from Minnesota
over 65 with heart
disease
Bob 5 Tanner 6 Mckay 5
Males from California
over 65 without heart
disease
Ashton 4 Roger 3 Steve 4
Females from Utah under
65 with heart disease
Keaton 5 Kris 6 Phil 6
Males from Texas under
65 with lung disease
Lynn 7 Ed 8 Kade 8
And Lynn, Ed, and Kade who are also matched in
terms of gender, residence, age and heart
condition.
114. June Hours
of sleep
July Hours of
sleep
August Hours of
sleep
Males from Minnesota
over 65 with heart
disease
Bob 5 Tanner 6 Mckay 5
Males from California
over 65 without heart
disease
Ashton 4 Roger 3 Steve 4
Females from Utah under
65 with heart disease
Keaton 5 Kris 6 Phil 6
Males from Texas under
65 with lung disease
Lynn 7 Ed 8 Kade 8
115. June Hours
of sleep
July Hours of
sleep
August Hours of
sleep
Males from Minnesota
over 65 with heart
disease
Bob 5 Tanner 6 Mckay 5
Males from California
over 65 without heart
disease
Ashton 4 Roger 3 Steve 4
Females from Utah under
65 with heart disease
Keaton 5 Kris 6 Phil 6
Males from Texas under
65 with lung disease
Lynn 7 Ed 8 Kade 8
And Lynn, Ed, and Kade who are also matched in
terms of gender, residence, age and heart
condition.
119. Once again, independent samples are samples that
have different research subjects.
Repeated samples have the same research subjects,
that are measured over multiple times.
120. Once again, independent samples are samples that
have different research subjects.
Repeated samples have the same research subjects,
that are measured over multiple times.
Repeated samples can have different research
subjects if those research subjects are matched in
some way. They are also measured over time.
121. In this Guided Practice you will be presented with two
word problems. You will be asked to determine if the
word problem is depicting an independent or repeated
measure samples.
123. Problem #1
Auto-engineers equip twelve cars with a special brand
of radial tires. These vehicles were then driven over a
test course. Then the same 12 cars were equipped
with regular belted tires and driven over the same
course. After each run, the cars’ miles per gallon was
measured.
124. Problem #1
Auto-engineers equip twelve cars with a special brand
of radial tires. These vehicles were then driven over a
test course. Then the same 12 cars were equipped
with regular belted tires and driven over the same
course. After each run, the cars’ miles per gallon was
measured.
Is this studying dealing with independent samples or
repeated measures?
125. Problem #1
Auto-engineers equip twelve cars with a special brand
of radial tires. These vehicles were then driven over a
test course. Then the same 12 cars were equipped
with regular belted tires and driven over the same
course. After each run, the cars’ miles per gallon was
measured.
Is this studying dealing with independent samples or
repeated measures?
A. independent samples
B. repeated measures
126. Problem #1
Auto-engineers equip twelve cars with a special brand
of radial tires. These vehicles were then driven over a
test course. Then the same 12 cars were equipped
with regular belted tires and driven over the same
course. After each run, the cars’ miles per gallon was
measured.
Is this studying dealing with independent samples or
repeated measures?
A. independent samples
B. repeated measures
127. Problem #1
Auto-engineers equip twelve cars with a special brand
of radial tires. These vehicles were then driven over a
test course. Then the same 12 cars were equipped
with regular belted tires and driven over the same
course. After each run, the cars’ miles per gallon was
measured.
The reason we are dealing with a repeated measures
sample here is because the SAME vehicles are being
tested twice. The only difference between the two
A. independent samples
B. repeated measures
times is the type of tires that were used.
128. Problem #1
Auto-engineers equip twelve cars with a special brand
of radial tires. These vehicles were then driven over a
test course. Then the same 12 cars were equipped
with regular belted tires and driven over the same
course. After each run, the cars’ miles per gallon was
measured.
The reason we are dealing with a repeated measures
sample here is because the SAME vehicles are being
tested twice. The only difference between the two
A. independent samples
B. repeated measures
times is the type of tires that were used.
129. Problem #1
Auto-engineers equip twelve cars with a special brand
of radial tires. These vehicles were then driven over a
test course. Then the same 12 cars were equipped
with regular belted tires and driven over the same
course. After each run, the cars’ miles per gallon was
measured.
The reason we are dealing with a repeated measures
sample here is because the SAME vehicles are being
tested twice. The only difference between the two
A. independent samples
B. repeated measures
times is the type of tires that were used.
130. Problem #1
Auto-engineers equip twelve cars with a special brand
of radial tires. These vehicles were then driven over a
test course. Then the same 12 cars were equipped
with regular belted tires and driven over the same
course. After each run, the cars’ miles per gallon was
measured.
The reason we are dealing with a repeated measures
sample here is because the SAME vehicles are being
tested twice. The only difference between the two
A. independent samples
B. repeated measures
times is the type of tires that were used.
131. Problem #1
Auto-engineers equip twelve cars with a special brand
of radial tires. These vehicles were then driven over a
test course. Then the same 12 cars were equipped
with regular belted tires and driven over the same
course. After each run, the cars’ miles per gallon was
measured.
First Time
The reason we are dealing with a repeated measures
sample here is because the SAME vehicles are being
tested twice. The only difference between the two
A. independent samples
B. repeated measures
times is the type of tires that were used.
132. Problem #1
Auto-engineers equip twelve cars with a special brand
of radial tires. These vehicles were then driven over a
test course. Then the same 12 cars were equipped
with regular belted tires and driven over the same
course. After each run, the cars’ miles per gallon was
measured.
The reason we are dealing with a repeated measures
sample here is because the SAME vehicles are being
tested twice. The only difference between the two
A. independent samples
B. repeated measures
times is the type of tires that were used.
133. Problem #1
Auto-engineers equip twelve cars with a special brand
of radial tires. These vehicles were then driven over a
test course. Then the same 12 cars were equipped
with regular belted tires and driven over the same
course. After each run, the cars’ miles per gallon was
measured.
The reason we are dealing with a repeated measures
sample here is because the SAME vehicles are being
tested twice. The only difference between the two
A. independent samples
B. repeated measures
times is the type of tires that were used.
134. Problem #1
Auto-engineers equip twelve cars with a special brand
of radial tires. These vehicles were then driven over a
test course. Then the same 12 cars were equipped
with regular belted tires and driven over the same
course. After each run, the cars’ miles per gallon was
measured.
Second Time
The reason we are dealing with a repeated measures
sample here is because the SAME vehicles are being
tested twice. The only difference between the two
A. independent samples
B. repeated measures
times is the type of tires that were used.
136. Problem #2
A manager wishes to determine whether the time
required to complete a certain task differs for the three
groups: Beginners, intermediate, and advanced trained
employees.
137. Problem #2
A manager wishes to determine whether the time
required to complete a certain task differs for the three
groups: Beginners, intermediate, and advanced trained
employees.
Is this studying dealing with independent samples or
repeated measures?
138. Problem #2
A manager wishes to determine whether the time
required to complete a certain task differs for the three
groups: Beginners, intermediate, and advanced trained
employees.
Is this studying dealing with independent samples or
repeated measures?
A. independent samples
B. repeated measures
139. Problem #2
A manager wishes to determine whether the time
required to complete a certain task differs for the three
groups: Beginners, intermediate, and advanced trained
employees.
Is this studying dealing with independent samples or
repeated measures?
A. independent samples
B. repeated measures
140. Problem #2
A manager wishes to determine whether the time
required to complete a certain task differs for the three
groups: Beginners, intermediate, and advanced trained
employees.
A. The independent reason we are samples
dealing with an independent
sample B. repeated here is measures
because we are comparing the time
required to complete certain tasks between three
different and unmatched groups.
141. Problem #2
A manager wishes to determine whether the time
required to complete a certain task differs for the three
groups: Beginners, intermediate, and advanced trained
employees.
A. The independent reason we are samples
dealing with an independent
sample B. repeated here is measures
because we are comparing the time
required to complete certain tasks between three
different and unmatched groups.
142. Problem #2
A manager wishes to determine whether the time
required to complete a certain task differs for the three
groups: Beginners, intermediate, and advanced trained
employees.
A. The independent reason we are samples
dealing with an independent
sample B. repeated here is measures
because we are comparing the time
required to complete certain tasks between three
different and unmatched groups.
143. Problem #2
A manager wishes to determine whether the time
required to complete a certain task differs for the three
groups: Beginners, intermediate, and advanced trained
employees.
A. The independent reason we are samples
dealing with an independent
sample B. repeated here is measures
because we are comparing the time
required to complete certain tasks between three
different and unmatched groups.
144. Problem #2
A manager wishes to determine whether the time
required to complete a certain task differs for the three
groups: Beginners, intermediate, and advanced trained
employees.
A. The independent reason we are samples
dealing with an independent
sample B. repeated here is measures
because we are comparing the time
required to complete certain tasks between three
different and unmatched groups.
145. Problem #2
A manager wishes to determine whether the time
required to complete a certain task differs for the three
groups: Beginners, intermediate, and advanced trained
employees.
A. The independent reason we are samples
dealing with an independent
sample B. repeated here is measures
because we are comparing the time
required to complete certain tasks between three
different and unmatched groups.
146. Look at the problem you are working on and
determine if the samples are independent or
repeated:
147. Look at the problem you are working on and
determine if the samples are independent or
repeated:
Independent Samples
Repeated Samples