The document describes how to report a partial correlation in APA format. It provides a template for reporting that when controlling for a covariate, the partial correlation between two variables is r = ___, p = ___. As an example, it states that when controlling for age, the partial correlation between intense fanaticism for a professional sports team and proximity to the city the team resides is r = .82, p = .000.
Here are the steps to find the quartiles for this data set:
1. Order the data from lowest to highest: 1, 1, 2, 2, 2, 2, 3, 3, 4, 4, 5, 5, 6, 7
2. The number of observations is 16. To find the quartiles, we split the data into 4 equal parts.
3. n/4 = 16/4 = 4
4. Q1 is the median of the lower half of the data, which is the 4th observation: 2
5. Q2 is the median of all the data, which is also the 8th observation: 3
6. Q3 is the median of the upper half
This document discusses measures of variability used to describe how spread out data values are from the mean or average. It defines and provides formulas for calculating range, variance, standard deviation, sample variance, sample standard deviation, population variance, population standard deviation, estimated population variance, and estimated population standard deviation. These measures are important in statistical analysis to understand the distribution of data values.
In statistics, the standard score is the (signed) number of standard deviations an observation or datum is above the mean. Thus, a positive standard score represents a datum above the mean, while a negative standard score represents a datum below the mean. It is a dimensionless quantity obtained by subtracting the population mean from an individual raw score and then dividing the difference by the population standard deviation. This conversion process is called standardizing or normalizing (however, "normalizing" can refer to many types of ratios; see normalization (statistics) for more).Standard scores are also called z-values, z-scores, normal scores, and standardized variables; the use of "Z" is because the normal distribution is also known as the "Z distribution". They are most frequently used to compare a sample to a standard normal deviate (standard normal distribution, with μ = 0 and σ = 1), though they can be defined without assumptions of normality.
The document describes how to report a partial correlation in APA format. It provides a template for reporting that when controlling for a covariate, the partial correlation between two variables is r = ___, p = ___. As an example, it states that when controlling for age, the partial correlation between intense fanaticism for a professional sports team and proximity to the city the team resides is r = .82, p = .000.
Here are the steps to find the quartiles for this data set:
1. Order the data from lowest to highest: 1, 1, 2, 2, 2, 2, 3, 3, 4, 4, 5, 5, 6, 7
2. The number of observations is 16. To find the quartiles, we split the data into 4 equal parts.
3. n/4 = 16/4 = 4
4. Q1 is the median of the lower half of the data, which is the 4th observation: 2
5. Q2 is the median of all the data, which is also the 8th observation: 3
6. Q3 is the median of the upper half
This document discusses measures of variability used to describe how spread out data values are from the mean or average. It defines and provides formulas for calculating range, variance, standard deviation, sample variance, sample standard deviation, population variance, population standard deviation, estimated population variance, and estimated population standard deviation. These measures are important in statistical analysis to understand the distribution of data values.
In statistics, the standard score is the (signed) number of standard deviations an observation or datum is above the mean. Thus, a positive standard score represents a datum above the mean, while a negative standard score represents a datum below the mean. It is a dimensionless quantity obtained by subtracting the population mean from an individual raw score and then dividing the difference by the population standard deviation. This conversion process is called standardizing or normalizing (however, "normalizing" can refer to many types of ratios; see normalization (statistics) for more).Standard scores are also called z-values, z-scores, normal scores, and standardized variables; the use of "Z" is because the normal distribution is also known as the "Z distribution". They are most frequently used to compare a sample to a standard normal deviate (standard normal distribution, with μ = 0 and σ = 1), though they can be defined without assumptions of normality.
The chi-square test is used to determine if there is a relationship between two categorical variables in two or more independent groups. It can be used when data is arranged in a contingency table with observed and expected frequencies. A sample problem demonstrates how to calculate chi-square by finding the difference between observed and expected counts, squaring these differences, dividing by the expected counts, and summing across all cells. Degrees of freedom and critical values from tables determine whether to reject or fail to reject the null hypothesis of independence. Larger tables can be partitioned into subtables to identify where differences lie. Guidelines are provided for when chi-square or Fisher's exact test should be used based on sample size and expected cell counts.
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.
This document discusses organizing and displaying data through statistics. It defines key terms like populations, samples, variables, and different types of variables. It then covers creating simple and grouped frequency distributions to organize raw data through tables. These distributions can be displayed visually through bar charts, histograms, and polygons to summarize the frequency of values within intervals or categories.
t test for single mean, t test for means of independent samples, t test for means of dependent sample ( Paired t test). Case study / Examples for hands on experience of how SPSS can be used for different hypothesis testing - t test.
The document discusses simple linear regression analysis. It provides definitions and formulas for simple linear regression, including that the regression equation is y = a + bx. An example is shown of using the stepwise method to determine if there is a significant relationship between number of absences (x) and grades (y) for students. The analysis finds a significant negative relationship, meaning more absences correlated with lower grades. Formulas are provided for calculating the slope, intercept, and testing significance of the regression model.
This document provides an overview of key concepts in descriptive statistics including graphical presentation of data. It discusses frequency distributions and different types of graphs used to describe categorical and numerical variables such as bar charts, pie charts, histograms, and scatter plots. Examples are provided to illustrate how to construct and interpret these various graphs. The goal is to explain how graphical displays of data can help summarize and convey information more clearly than raw numbers alone.
This document discusses evaluating the normality of data distributions. It covers probability, normal distributions, z-scores, empirical rules, and tests for skewness and kurtosis. Normal distributions are symmetric and bell-shaped. The normality of data can be determined using z-scores and empirical rules. Skewness measures asymmetry in a distribution, while kurtosis measures tail weight. Normality tests like Shapiro-Wilk can determine if a dataset comes from a normal distribution.
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.
Binary logistic regression analysis is used to predict a dichotomous dependent variable from continuous and/or categorical independent variables. SPSS is used to conduct binary logistic regression by entering the dependent variable as 1/0 and independent variables as predictors, and the output provides coefficients, odds ratios, classification tables, and goodness of fit tests. Factors like multicollinearity between predictors and sample size need to be considered to develop the best fitting and most predictive logistic regression model.
Pearson Correlation, Spearman Correlation &Linear RegressionAzmi Mohd Tamil
This document discusses correlation and linear regression. It defines correlation as a statistic that measures the strength and direction of the linear relationship between two continuous variables. Positive correlation indicates that as one variable increases, so does the other. Negative correlation means the variables are inversely related. Linear regression can be used to predict a continuous outcome variable based on a continuous predictor variable using the regression equation y=a+bx. The regression line minimizes the sum of squared differences between the data points and the line. The slope coefficient b indicates the strength of the linear prediction and can be tested for significance.
This document discusses the sign test, a nonparametric statistical method used to test for differences between paired observations. It defines the sign test and describes different types, including two-sided and one-sided tests. Examples are provided to illustrate how to calculate and interpret the sign test for a two samples, median of a single sample, and preference between two products. The conclusion discusses when nonparametric vs parametric tests are most appropriate based on sample size and assumption violations.
This document provides an overview of hypothesis testing fundamentals. It defines a hypothesis as an educated guess about a population parameter that is tested through experimentation. The document outlines the key components of hypothesis testing, including the null and alternative hypotheses, levels of significance, types of errors, p-values, one-tailed and two-tailed tests, and degrees of freedom. It also discusses parametric and non-parametric tests and the steps involved in conducting hypothesis testing, from defining the problem to making a statistical decision.
Here are the steps to solve this problem:
1) State the null (H0) and alternative (Ha) hypotheses:
H0: μ = 60
Ha: μ < 60
2) Set the significance level: α = 0.05
3) Find the critical value for a one-tailed test: Zc = -1.65
4) Calculate the test statistic: Z = (58 - 60)/√(40) = -2
5) Make a decision: Since Z = -2 < -1.65, reject H0.
6) State your conclusion: At the 5% significance level, there is sufficient evidence to conclude that the mean battery life is less
The chi-square test is used to determine if there is a relationship between two categorical variables in two or more independent groups. It can be used when data is arranged in a contingency table with observed and expected frequencies. A sample problem demonstrates how to calculate chi-square by finding the difference between observed and expected counts, squaring these differences, dividing by the expected counts, and summing across all cells. Degrees of freedom and critical values from tables determine whether to reject or fail to reject the null hypothesis of independence. Larger tables can be partitioned into subtables to identify where differences lie. Guidelines are provided for when chi-square or Fisher's exact test should be used based on sample size and expected cell counts.
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.
This document discusses organizing and displaying data through statistics. It defines key terms like populations, samples, variables, and different types of variables. It then covers creating simple and grouped frequency distributions to organize raw data through tables. These distributions can be displayed visually through bar charts, histograms, and polygons to summarize the frequency of values within intervals or categories.
t test for single mean, t test for means of independent samples, t test for means of dependent sample ( Paired t test). Case study / Examples for hands on experience of how SPSS can be used for different hypothesis testing - t test.
The document discusses simple linear regression analysis. It provides definitions and formulas for simple linear regression, including that the regression equation is y = a + bx. An example is shown of using the stepwise method to determine if there is a significant relationship between number of absences (x) and grades (y) for students. The analysis finds a significant negative relationship, meaning more absences correlated with lower grades. Formulas are provided for calculating the slope, intercept, and testing significance of the regression model.
This document provides an overview of key concepts in descriptive statistics including graphical presentation of data. It discusses frequency distributions and different types of graphs used to describe categorical and numerical variables such as bar charts, pie charts, histograms, and scatter plots. Examples are provided to illustrate how to construct and interpret these various graphs. The goal is to explain how graphical displays of data can help summarize and convey information more clearly than raw numbers alone.
This document discusses evaluating the normality of data distributions. It covers probability, normal distributions, z-scores, empirical rules, and tests for skewness and kurtosis. Normal distributions are symmetric and bell-shaped. The normality of data can be determined using z-scores and empirical rules. Skewness measures asymmetry in a distribution, while kurtosis measures tail weight. Normality tests like Shapiro-Wilk can determine if a dataset comes from a normal distribution.
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.
Binary logistic regression analysis is used to predict a dichotomous dependent variable from continuous and/or categorical independent variables. SPSS is used to conduct binary logistic regression by entering the dependent variable as 1/0 and independent variables as predictors, and the output provides coefficients, odds ratios, classification tables, and goodness of fit tests. Factors like multicollinearity between predictors and sample size need to be considered to develop the best fitting and most predictive logistic regression model.
Pearson Correlation, Spearman Correlation &Linear RegressionAzmi Mohd Tamil
This document discusses correlation and linear regression. It defines correlation as a statistic that measures the strength and direction of the linear relationship between two continuous variables. Positive correlation indicates that as one variable increases, so does the other. Negative correlation means the variables are inversely related. Linear regression can be used to predict a continuous outcome variable based on a continuous predictor variable using the regression equation y=a+bx. The regression line minimizes the sum of squared differences between the data points and the line. The slope coefficient b indicates the strength of the linear prediction and can be tested for significance.
This document discusses the sign test, a nonparametric statistical method used to test for differences between paired observations. It defines the sign test and describes different types, including two-sided and one-sided tests. Examples are provided to illustrate how to calculate and interpret the sign test for a two samples, median of a single sample, and preference between two products. The conclusion discusses when nonparametric vs parametric tests are most appropriate based on sample size and assumption violations.
This document provides an overview of hypothesis testing fundamentals. It defines a hypothesis as an educated guess about a population parameter that is tested through experimentation. The document outlines the key components of hypothesis testing, including the null and alternative hypotheses, levels of significance, types of errors, p-values, one-tailed and two-tailed tests, and degrees of freedom. It also discusses parametric and non-parametric tests and the steps involved in conducting hypothesis testing, from defining the problem to making a statistical decision.
Here are the steps to solve this problem:
1) State the null (H0) and alternative (Ha) hypotheses:
H0: μ = 60
Ha: μ < 60
2) Set the significance level: α = 0.05
3) Find the critical value for a one-tailed test: Zc = -1.65
4) Calculate the test statistic: Z = (58 - 60)/√(40) = -2
5) Make a decision: Since Z = -2 < -1.65, reject H0.
6) State your conclusion: At the 5% significance level, there is sufficient evidence to conclude that the mean battery life is less
14. סולמות מדידה איל רבין - מבוא לסטטיסטיקה eyal.rabin [at] gmail.com הסולם שמי - נומינלי סדר - אורדינלי מרווחי - אינטרוולי מנה – רציו תכונות זהות זהות סדר זהות סדר רווח ( בכמה ) 0 שרירותי זהות סדר רווח ( בכמה ) יחס ( פי כמה ) 0 מוחלט
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23. סולם אורדינלי - סדר תדירות צפייה בחדשות ותכניות אקטואליה בטלוויזיה פאי / עוגה ( פחות מקובל כי העיגול לא ממחיש את הסדר בין הערכים ) איל רבין - מבוא לסטטיסטיקה eyal.rabin [at] gmail.com
24. סולם כמותי ( רווח ומנה ) בדיד בר , עמודות או קו המתאר מגמה איל רבין - מבוא לסטטיסטיקה eyal.rabin [at] gmail.com משך צפייה ממוצע בז ' אנרים שונים בטלוויזיה ( בדקות ) במהלך תהליך והאינתיפאדה השנייה
25. סולם כמותי ( רווח ומנה ) רציף היסטוגרמה שכיחות רמת הקולקטיביזם של ההורה איל רבין - מבוא לסטטיסטיקה eyal.rabin [at] gmail.com
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38. לציון התקן שני מאפיינים : הסימן מראה את הכיוון יחסית לממוצע הערך המספרי מציין את המרחק מהממוצע סימן שלילי = הנתון קטן מהממוצע סימן חיובי = הנתון גדול מהממוצע אפס = הנתון שווה לממוצע ככל שהערך של ציון התקן גדול יותר בערך המוחלט שלו ( חיובי או שלילי ), הנתון רחוק יותר מהממוצע . איל רבין - מבוא לסטטיסטיקה eyal.rabin [at] gmail.com