Quick reminder inferential vs descriptive
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Inferential statistics generalize information from a random sample to make predictions about an entire population, while descriptive statistics do not generalize because they simply describe characteristics of the entire population such as central tendency, spread, and symmetry without making broader generalizations.
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Quick reminder inferential vs descriptive
Inferential statistics generalize information from a random sample to make predictions about an entire population, while descriptive statistics do not generalize because they simply describe characteristics of the entire population itself, such as measures of central tendency, spread, and symmetry.
Quick reminder inferential vs descriptive
Inferential statistics can generalize information from a sample to a larger population, while descriptive statistics do not generalize as they deal directly with the entire population. This document discusses the difference between inferential statistics, which can describe characteristics of a larger population based on a random sample, and descriptive statistics, which only describe the actual characteristics of the sample or population studied without generalization.
Quick reminder nonparametric tests
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.
Reporting chi square goodness of fit test of independence in apa
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.
Null hypothesis for a chi-square goodness of fit test
The document discusses how to write a null hypothesis for a chi-square goodness of fit test. It provides an example of a poll that surveyed voters in Connecticut on their party affiliation (Republican or Democrat). The expected distribution was 40% Republican and 60% Democrat. The null hypothesis is stated as: The party affiliation of Republican/Democrat occur at a .4/.6 probability in Connecticut.
Reporting a single sample t-test
This document provides guidance on reporting the results of a single sample t-test in APA format. It includes templates for describing the test and population in the introduction and reporting the mean, standard deviation, t-value and significance in the results. An example is given of a hypothetical single sample t-test comparing IQ scores of people who eat broccoli regularly to the general population.
Quick reminder sample size
The document discusses sample size and recommends selecting the "sample sizes equal to or greater than 30" option if the sample size of both groups in a problem is equal to or greater than 30, otherwise selecting the "sample sizes less than 30" option. It notes that this recommendation provides the foundation for all inferential statistics and directs the reader to additional resources for a more thorough explanation of why sample size is important.
Quick tip sample size
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.
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Quick reminder inferential vs descriptive
Inferential statistics generalize information from a random sample to make predictions about an entire population, while descriptive statistics do not generalize because they simply describe characteristics of the entire population itself, such as measures of central tendency, spread, and symmetry.
Quick reminder inferential vs descriptive
Inferential statistics can generalize information from a sample to a larger population, while descriptive statistics do not generalize as they deal directly with the entire population. This document discusses the difference between inferential statistics, which can describe characteristics of a larger population based on a random sample, and descriptive statistics, which only describe the actual characteristics of the sample or population studied without generalization.
Quick reminder nonparametric tests
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.
Reporting chi square goodness of fit test of independence in apa
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.
Null hypothesis for a chi-square goodness of fit test
The document discusses how to write a null hypothesis for a chi-square goodness of fit test. It provides an example of a poll that surveyed voters in Connecticut on their party affiliation (Republican or Democrat). The expected distribution was 40% Republican and 60% Democrat. The null hypothesis is stated as: The party affiliation of Republican/Democrat occur at a .4/.6 probability in Connecticut.
Reporting a single sample t-test
This document provides guidance on reporting the results of a single sample t-test in APA format. It includes templates for describing the test and population in the introduction and reporting the mean, standard deviation, t-value and significance in the results. An example is given of a hypothetical single sample t-test comparing IQ scores of people who eat broccoli regularly to the general population.
Quick reminder sample size
The document discusses sample size and recommends selecting the "sample sizes equal to or greater than 30" option if the sample size of both groups in a problem is equal to or greater than 30, otherwise selecting the "sample sizes less than 30" option. It notes that this recommendation provides the foundation for all inferential statistics and directs the reader to additional resources for a more thorough explanation of why sample size is important.
Quick tip sample size
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.
Single or multiple linear practice problems
The document describes two practice problems involving predicting systolic blood pressure (BPR) from patient data. The first problem involves using age alone to predict BPR, which requires single linear regression. The second problem involves using both age and gender to predict BPR, requiring multiple linear regression as it has two predictor variables.
Non parametric relationship - practice problems
The document describes several practice problems involving determining the type of relationship between different variables. The first problem involves determining the relationship between patients' age (skewed variable) and systolic blood pressure (skewed variable). The second examines the relationship between blood pressure (scaled variable) and gender (dichotomous variable). The third looks at the relationship between religious affiliation (dichotomous variable) and depression status (dichotomous variable). The fourth analyzes the relationship between age (scaled variable) and nationally ranked blood pressure (ordinal variable). Finally, the fifth considers the link between gender (dichotomous variable) and support for a proposition (dichotomous variable).
Predictive or arbitrary relationship practice problems
The director of a health clinic asked if patients' age influences their systolic blood pressure. The data shows higher blood pressure levels generally associated with older patient ages, indicating a predictive relationship between age and blood pressure.
The director also asked if there is a relationship between systolic blood pressure and stress levels. The data presented suggests blood pressure levels tend to be higher with greater reported stress levels, indicative of a predictive relationship between stress and blood pressure.
The director provided two datasets and asked if age influences blood pressure in one case and if there is a relationship between blood pressure and stress in the other, with both situations presenting predictive relationships between the variables based on the information given.
Rank order relationship (ties) practice problems
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.
Parametric or non parametric relationship practice problems
1. The document presents 6 practice problems involving determining whether to use parametric or non-parametric methods to analyze relationships between variables.
2. The problems involve different variable types including continuous/scaled, categorical/nominal, and ranked variables.
3. The correct method - parametric or non-parametric - depends on whether the variables are normally distributed and meet other assumptions as well as whether they are continuous or categorical.
Non parametric relationship (names) - practice problems
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.
Running & Reporting an One-way ANCOVA in SPSS
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.
Quick reminder parametric - nonparametric relationship
This document provides guidance on choosing between parametric and non-parametric statistical tests. It first considers whether both variables are scaled and their distributions are normal; if so, a parametric test is suitable. If one variable is ordinal or nominal, or one distribution is skewed, a non-parametric test should be used. It further narrows the choice depending on whether both sample sizes are greater than 30.
Tutorial parametric tests
Parametric methods examine sample statistics as representations of population parameters when the data is scaled, the distribution is either normal or skewed, and the sample size is greater than or equal to 30 subjects. Parametric methods assume the data follows certain statistical distributions.
Null hypothesis for Pearson Correlation (independence)
The document discusses writing null hypotheses for Pearson correlation tests. It provides examples of null hypotheses for two problems: 1) determining if student ACT scores and GPAs are independent, and 2) determining if depression scores and sense of belonging scores are independent. The null hypothesis template is "There is no statistically significant relationship between variable 1 and variable 2". For the first problem, the null hypothesis is "There is no statistically significant relationship between student ACT scores and grade point averages". For the second problem, the null hypothesis is "There is no statistically significant relationship between depression scores and sense of belonging scores".
Quick reminder nonparametric tests
Non-parametric methods are statistical techniques used to analyze sample statistics as representations of population parameters when the distribution is skewed with less than 30 subjects, the data is ordinal or nominal, or the data consists of rankings like percentiles. Non-parametric methods do not rely on assumptions about the distribution of variables and are appropriate for small sample sizes or non-numerical data types.
What is a one sample wilcoxon test
The document explains the one-sample Wilcoxon test, which is used to determine if a sample is statistically similar to or different from a population. It provides an example comparing IQ scores, where researchers took a random sample of 20 people and want to know if the sample's mean of 70 is representative of the overall population's mean of 100. The one-sample Wilcoxon test compares the sample and population medians to determine the probability that the sample and population are statistically similar or different.
Null hypothesis for single linear regression
The document discusses the null hypothesis for a single linear regression analysis. It explains that the null hypothesis states that there is no effect or relationship between the independent and dependent variables. As an example, if investigating the relationship between hours of sleep and ACT scores, the null hypothesis would be: "There will be no significant prediction of ACT scores by hours of sleep." The document provides a template for writing the null hypothesis in terms of the specific independent and dependent variables being analyzed.
Reporting Pearson Correlation Test of Independence in APA
A Pearson correlation test of independence was conducted to determine if student height and GPA were related. A weak correlation was found between height and GPA (r = .217, p > .05), indicating that student height and GPA are independent of each other.
Quick reminder parametric - nonparametric difference
This document provides guidance on choosing between parametric and non-parametric tests based on the characteristics of results data. It asks whether the data is scaled and normally distributed, and if the sample sizes are greater than 30, to determine if a parametric or non-parametric test is more appropriate.
Tutorial normal v skew distributions
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.
Quickreminder nature of the data (relationship)
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.
Reporting a paired sample t test
The document provides guidance on reporting the results of a paired sample t-test in APA format. It includes templates for reporting the study design, results, and statistical analysis. Key details include reporting the means, standard deviations, and standard errors for each condition. It also notes reporting the t-statistic, degrees of freedom, and significance level based on the t-test output.
Tutorial parametric v. non-parametric
This presentation discusses parametric and non-parametric methods for analyzing relationships between variables. Parametric methods can be used when sample data is normally distributed and scaled, representing population parameters. They involve examining relationships between variables like death anxiety and religiosity through statistical tests. Non-parametric methods do not require normal distribution or scaling and can be used as an alternative.
Tutorial repeated measures ANOVA
A repeated measures ANOVA is used to test whether a single group of people change over time by comparing distributions from the same group at different time periods, rather than comparing distributions from different groups. The overall F-ratio reveals if there are differences among time periods, and post hoc tests identify exactly where the differences occurred. In contrast, a one-way ANOVA compares distributions between two or more different groups to determine if there are statistical differences between them.
Diff rel gof-fit - jejit - practice (5)
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.
Learn About Range - Copyright updated
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.
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Single or multiple linear practice problems
The document describes two practice problems involving predicting systolic blood pressure (BPR) from patient data. The first problem involves using age alone to predict BPR, which requires single linear regression. The second problem involves using both age and gender to predict BPR, requiring multiple linear regression as it has two predictor variables.
Non parametric relationship - practice problems
The document describes several practice problems involving determining the type of relationship between different variables. The first problem involves determining the relationship between patients' age (skewed variable) and systolic blood pressure (skewed variable). The second examines the relationship between blood pressure (scaled variable) and gender (dichotomous variable). The third looks at the relationship between religious affiliation (dichotomous variable) and depression status (dichotomous variable). The fourth analyzes the relationship between age (scaled variable) and nationally ranked blood pressure (ordinal variable). Finally, the fifth considers the link between gender (dichotomous variable) and support for a proposition (dichotomous variable).
Predictive or arbitrary relationship practice problems
The director of a health clinic asked if patients' age influences their systolic blood pressure. The data shows higher blood pressure levels generally associated with older patient ages, indicating a predictive relationship between age and blood pressure.
The director also asked if there is a relationship between systolic blood pressure and stress levels. The data presented suggests blood pressure levels tend to be higher with greater reported stress levels, indicative of a predictive relationship between stress and blood pressure.
The director provided two datasets and asked if age influences blood pressure in one case and if there is a relationship between blood pressure and stress in the other, with both situations presenting predictive relationships between the variables based on the information given.
Rank order relationship (ties) practice problems
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.
Parametric or non parametric relationship practice problems
1. The document presents 6 practice problems involving determining whether to use parametric or non-parametric methods to analyze relationships between variables.
2. The problems involve different variable types including continuous/scaled, categorical/nominal, and ranked variables.
3. The correct method - parametric or non-parametric - depends on whether the variables are normally distributed and meet other assumptions as well as whether they are continuous or categorical.
Non parametric relationship (names) - practice problems
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.
Running & Reporting an One-way ANCOVA in SPSS
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.
Quick reminder parametric - nonparametric relationship
This document provides guidance on choosing between parametric and non-parametric statistical tests. It first considers whether both variables are scaled and their distributions are normal; if so, a parametric test is suitable. If one variable is ordinal or nominal, or one distribution is skewed, a non-parametric test should be used. It further narrows the choice depending on whether both sample sizes are greater than 30.
Tutorial parametric tests
Parametric methods examine sample statistics as representations of population parameters when the data is scaled, the distribution is either normal or skewed, and the sample size is greater than or equal to 30 subjects. Parametric methods assume the data follows certain statistical distributions.
Null hypothesis for Pearson Correlation (independence)
The document discusses writing null hypotheses for Pearson correlation tests. It provides examples of null hypotheses for two problems: 1) determining if student ACT scores and GPAs are independent, and 2) determining if depression scores and sense of belonging scores are independent. The null hypothesis template is "There is no statistically significant relationship between variable 1 and variable 2". For the first problem, the null hypothesis is "There is no statistically significant relationship between student ACT scores and grade point averages". For the second problem, the null hypothesis is "There is no statistically significant relationship between depression scores and sense of belonging scores".
Quick reminder nonparametric tests
Non-parametric methods are statistical techniques used to analyze sample statistics as representations of population parameters when the distribution is skewed with less than 30 subjects, the data is ordinal or nominal, or the data consists of rankings like percentiles. Non-parametric methods do not rely on assumptions about the distribution of variables and are appropriate for small sample sizes or non-numerical data types.
What is a one sample wilcoxon test
The document explains the one-sample Wilcoxon test, which is used to determine if a sample is statistically similar to or different from a population. It provides an example comparing IQ scores, where researchers took a random sample of 20 people and want to know if the sample's mean of 70 is representative of the overall population's mean of 100. The one-sample Wilcoxon test compares the sample and population medians to determine the probability that the sample and population are statistically similar or different.
Null hypothesis for single linear regression
The document discusses the null hypothesis for a single linear regression analysis. It explains that the null hypothesis states that there is no effect or relationship between the independent and dependent variables. As an example, if investigating the relationship between hours of sleep and ACT scores, the null hypothesis would be: "There will be no significant prediction of ACT scores by hours of sleep." The document provides a template for writing the null hypothesis in terms of the specific independent and dependent variables being analyzed.
Reporting Pearson Correlation Test of Independence in APA
A Pearson correlation test of independence was conducted to determine if student height and GPA were related. A weak correlation was found between height and GPA (r = .217, p > .05), indicating that student height and GPA are independent of each other.
Quick reminder parametric - nonparametric difference
This document provides guidance on choosing between parametric and non-parametric tests based on the characteristics of results data. It asks whether the data is scaled and normally distributed, and if the sample sizes are greater than 30, to determine if a parametric or non-parametric test is more appropriate.
Tutorial normal v skew distributions
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.
Quickreminder nature of the data (relationship)
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.
Reporting a paired sample t test
The document provides guidance on reporting the results of a paired sample t-test in APA format. It includes templates for reporting the study design, results, and statistical analysis. Key details include reporting the means, standard deviations, and standard errors for each condition. It also notes reporting the t-statistic, degrees of freedom, and significance level based on the t-test output.
Tutorial parametric v. non-parametric
This presentation discusses parametric and non-parametric methods for analyzing relationships between variables. Parametric methods can be used when sample data is normally distributed and scaled, representing population parameters. They involve examining relationships between variables like death anxiety and religiosity through statistical tests. Non-parametric methods do not require normal distribution or scaling and can be used as an alternative.
Tutorial repeated measures ANOVA
A repeated measures ANOVA is used to test whether a single group of people change over time by comparing distributions from the same group at different time periods, rather than comparing distributions from different groups. The overall F-ratio reveals if there are differences among time periods, and post hoc tests identify exactly where the differences occurred. In contrast, a one-way ANOVA compares distributions between two or more different groups to determine if there are statistical differences between them.
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Predictive or arbitrary relationship practice problems
Predictive or arbitrary relationship practice problems
Parametric or non parametric relationship practice problems
Parametric or non parametric relationship practice problems
Non parametric relationship (names) - practice problems
Non parametric relationship (names) - practice problems
Quick reminder parametric - nonparametric relationship
Quick reminder parametric - nonparametric relationship
Null hypothesis for Pearson Correlation (independence)
Null hypothesis for Pearson Correlation (independence)
Reporting Pearson Correlation Test of Independence in APA
Reporting Pearson Correlation Test of Independence in APA
Quick reminder parametric - nonparametric difference
Quick reminder parametric - nonparametric difference
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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.
Learn About Range - Copyright updated
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Diff rel ind-fit practice - Copyright Updated
The document provides explanations and examples for different types of statistical questions:
- Difference questions compare two or more groups on an outcome.
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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 updated
- The document discusses determining whether distributions are normal or skewed
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- 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
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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.
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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 updated
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Nature of the data (descriptive) - Copyright updated
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.
Dichotomous or scaled
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.
Skewed less than 30 (ties)
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.
Skewed sample size less than 30
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.
Ordinal (ties)
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.
Ordinal and nominal
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.
Relationship covariates
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.
Relationship nature of data
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.
Number of variables (predictive)
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.
Levels of the iv
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.
Independent variables (2)
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.
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Presented at the Stoke Newington Literary Festival on 9th June 2024
www.StokeNewingtonHistory.com
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Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
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https://app.box.com/s/y977uz6bpd3af4qsebv7r9b7s21935vdLAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
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centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
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cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
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