1) The document describes steps to prepare data in Microsoft Excel for analysis in IBM SPSS. It involves changing sex variables from text to numeric, exporting the Excel file to a text file for SPSS, and opening the file in SPSS.
2) Descriptive statistics are calculated in SPSS, including measures of central tendency, variability, and normality. Percentiles and z-scores are also computed.
3) Further computations include p-values using z-scores and percentile ranks. The outputs are examined and files are saved.
This document provides a tutorial on conducting and interpreting a multiple linear regression analysis in SPSS. It contains two sections - the first outlines the steps to specify a regression analysis in SPSS using sample data. The second section interprets example SPSS output, including descriptive statistics, bivariate correlations, model summary, ANOVA table, and coefficients output. It also provides a guide for writing up the results in APA style.
This document provides an introduction to SPSS, including descriptions of the four windows in SPSS, basics of managing data files, and basic analysis functions. It discusses the data editor, output viewer, syntax editor, and script windows. It covers opening SPSS, defining and managing variables, saving and sorting data, transforming variables through computations, and conducting basic analyses like frequencies, descriptives, and linear regression. Examples provided include creating new variables, sorting by height, and analyzing relationships between education level and starting salary.
This document provides an overview of using the SPSS statistical package for data analysis. It discusses the four main windows in SPSS - the data editor, output viewer, syntax editor, and script window. It also covers the basics of managing data files, including opening SPSS, defining variables, and saving data. Finally, it introduces some basic analysis techniques in SPSS like frequencies, descriptives, and linear regression analysis.
This document provides an introduction and overview of using the statistical software package SPSS. It discusses opening SPSS and navigating the main windows, including the data editor, variable view, and output viewer. It also demonstrates how to enter sample data on student characteristics, sort and transform variables, and conduct basic analyses like frequencies, descriptives, and linear regression. Examples provided include sorting data by height, calculating new variables like the natural log and square root of height, and analyzing the relationship between education level and beginning salary.
1) The document describes steps to prepare data in Microsoft Excel for analysis in IBM SPSS. It involves changing sex variables from text to numeric, exporting the Excel file to a text file for SPSS, and opening the file in SPSS.
2) Descriptive statistics are calculated in SPSS, including measures of central tendency, variability, and normality. Percentiles and z-scores are also computed.
3) Further computations include p-values using z-scores and percentile ranks. The outputs are examined and files are saved.
This document provides a tutorial on conducting and interpreting a multiple linear regression analysis in SPSS. It contains two sections - the first outlines the steps to specify a regression analysis in SPSS using sample data. The second section interprets example SPSS output, including descriptive statistics, bivariate correlations, model summary, ANOVA table, and coefficients output. It also provides a guide for writing up the results in APA style.
This document provides an introduction to SPSS, including descriptions of the four windows in SPSS, basics of managing data files, and basic analysis functions. It discusses the data editor, output viewer, syntax editor, and script windows. It covers opening SPSS, defining and managing variables, saving and sorting data, transforming variables through computations, and conducting basic analyses like frequencies, descriptives, and linear regression. Examples provided include creating new variables, sorting by height, and analyzing relationships between education level and starting salary.
This document provides an overview of using the SPSS statistical package for data analysis. It discusses the four main windows in SPSS - the data editor, output viewer, syntax editor, and script window. It also covers the basics of managing data files, including opening SPSS, defining variables, and saving data. Finally, it introduces some basic analysis techniques in SPSS like frequencies, descriptives, and linear regression analysis.
This document provides an introduction and overview of using the statistical software package SPSS. It discusses opening SPSS and navigating the main windows, including the data editor, variable view, and output viewer. It also demonstrates how to enter sample data on student characteristics, sort and transform variables, and conduct basic analyses like frequencies, descriptives, and linear regression. Examples provided include sorting data by height, calculating new variables like the natural log and square root of height, and analyzing the relationship between education level and beginning salary.
This document provides an introduction and overview of how to use the statistical software package SPSS. It discusses getting started with SPSS, opening and viewing data files, coding variables, and performing basic descriptive statistics. Specific tasks covered include entering and labeling variables, assigning value labels, handling missing data, generating frequency tables and graphs like histograms and box plots, recoding variables, and using the Compute function to calculate new variables.
This document provides instructions for first-time users of the EGP gradebook program on how to set up classes, categories, assignments, input grades, and print reports. It covers how to connect to the server, open an existing gradebook, customize class and assignment options, enter scores and grades, filter views, rename classes, and combine grades across terms.
This document provides an introduction and guidelines for applied statistics and statistical methods. It discusses topics like data handling, file handling, and graphs. For data handling, it describes how to recode variables into different groups and compute new variables using algebraic functions of existing variables. For file handling, it discusses how to select only certain cases, such as males, and split a file into groups. Finally, it demonstrates how to create histograms and boxplots to explore the distribution of variable scores and identify any outliers in the data.
We would like to introduce sampling software which costs just 10 USD. Sampling is statistical software designed to calculate sampling computation easily such as stratified sampling, cluster sampling, sampling with varying probability and etc. You can download free 7 times running trial license here:
http://www.sampling-software.com
This document provides an overview of SPSS and how to perform basic analyses in it. It discusses the four main windows in SPSS: the data editor, output viewer, syntax editor, and script window. It then covers how to open and manage data files, define variables, sort and transform data. The document concludes by demonstrating how to conduct frequency analyses, descriptive statistics, linear regression analyses, and plot regression lines in SPSS through both the graphical user interface and syntax editor.
This document provides instructions for performing various statistical analyses and data management tasks in SPSS, including sorting data, selecting cases, splitting files, merging files, visual binning, frequencies analysis, descriptive statistics, cross tabulation and chi-square tests, independent samples t-tests, and one-way ANOVA. The document is authored by trainers from the Department of Applied Statistics at the University of Rwanda and dated December 6, 2014.
The document discusses the steps for conducting a response surface methodology (RSM) experiment using central composite design (CCD). It involves determining independent and dependent variables, selecting an appropriate CCD, conducting the experiment runs according to the design, analyzing the data using statistical methods to develop a mathematical model and check its adequacy, and using the model to optimize responses. Key aspects of RSM and CCD covered include developing the design, analyzing results through ANOVA and regression, and checking model validity.
This document discusses linear programming (LP), including:
1. LP is a quantitative tool used by managers to obtain optimal solutions to problems with restrictions or limitations. Applications include production scheduling and facility location planning.
2. An LP model has an objective function that represents profit/cost, decision variables representing amounts, constraints limiting alternatives, and parameters.
3. Excel's Solver tool can be used to solve LP problems by specifying the target cell, decision variable cells, constraints, and solving to find optimal values.
Software packages for statistical analysis - SPSSANAND BALAJI
This document provides an overview of the Statistical Package for Social Sciences (SPSS). It discusses what SPSS is, how to define and enter variables, and the four main windows in SPSS including the data editor, output viewer, syntax editor, and script window. Basic functions like frequencies analysis, descriptives, and linear regression are also introduced.
De vry math 399 all ilabs latest 2016 novemberlenasour
This document provides information about obtaining assistance with coursework from an online service called ACEHOMEWORK.NET. It lists various courses and assignments they can help with, such as accounting, marketing, finance, economics, mathematics, statistics, programming, and more. It emphasizes they can help students get an A grade and provide original, plagiarism-free work by the deadline. Contact information is provided to obtain more details and pricing information.
This document provides instructions for conducting a linear regression analysis in SPSS using one predictor and one criterion variable. It demonstrates how to perform the analysis using data on fertility rates and infant mortality rates in different countries. Key steps include selecting the variables, running the analysis, and interpreting output sections like the model summary, ANOVA table, coefficients, and interactive scatterplot with a regression line showing the relationship between the variables.
This document provides an overview of the Statistical Package for Social Sciences (SPSS) software. It describes the main components and windows in SPSS, including the data window, variable view window, output window, and chart editor window. It also outlines several statistical techniques that can be performed in SPSS, such as descriptive statistics, correlations, t-tests, and chi-square tests of independence. SPSS is a tool that allows users to manage and analyze data, as well as generate graphs and conduct a wide range of statistical procedures.
This document provides an introduction to using the Excel Solver tool to solve optimization problems. It explains how to enable the Solver add-in, the four main steps to solving a linear problem using Solver: 1) organizing the problem information, 2) setting up the problem in a spreadsheet, 3) running Solver, and 4) interpreting the results. It also includes an example problem about maximizing profits from manufacturing different products with limited resources, and shows how to set up and solve this problem using Solver.
Using excel to convert raw score to z scoreSandra Nicks
The document provides instructions for converting raw scores to z-scores in Excel. It describes entering raw scores in a column, using the Data Analysis tool to calculate the mean and standard deviation of the raw scores. It then explains the formula to convert each raw score to a z-score by taking the raw score minus the mean and dividing by the standard deviation. The user is guided to enter this formula in a cell and copy it down to convert all raw scores to z-scores.
ANOVA, Chi-Square Tests, and RegressionComplete the followin.docxamrit47
ANOVA, Chi-Square Tests, and Regression
Complete the following problems within this Word document. Do not submit other files. Show your work for problem sets that require calculations. Ensure that your answer to each problem is clearly visible. You may want to highlight your answer or use a different type color to set it apart.
ANOVA
Problem Set 4.1: Critical Value
Criterion:
Explain the relationship between
k
and power based on calculated
k
values.
Instructions:
Read the following and answer the questions.
Work through the following and write down what you see in the
F-
table. This will help familiarize you with the table.
The
F-
table: The degrees of freedom for the numerator (
k
− 1) are across the columns; the degrees of freedom for the denominator (
N
−
k
) are across the rows in the table. A separate table is included for a .05 and .01 level of significance.
Increasing the levels of the independent variable (
k
):
Suppose we have a sample size of 24 participants (
N
= 24). Record the critical values given the following values for
k
:
.05
.01
k
= 2
k
= 4
k
= 6
k
= 8
___
___
___
___
___
___
___
___
As
k
increases (from 1 to 8), does the critical value increase or decrease? Based on your answer, explain how
k
is related to power.
Problem Set 4.2: One-way ANOVA in SPSS
Criterion:
Calculate an ANOVA in SPSS.
Data:
The following is the amount of fat (in grams) consumed in a buffet-style lunch among professional bodybuilders under conditions of high, moderate, and low stress:
Stress Levels
High
Moderate
Low
10
9
9
7
4
4
8
7
6
12
6
5
6
8
7
Instructions:
Complete the following steps:
a. Open SPSS and open a
New DataSet
.
b. Click the
Variable View
tab at the bottom and enter
Stress
and enter
Fat
as the variables. Click the
Values
box for the
Stress
row and define 1 as high, 2 as medium, and 3 as low.
c. Enter the data. For example, type 1 in row 1 under
Stress
and type 10 in row 1 under
Fat
. Continue typing in all the data. Please remember to change to 2 in column 1 when the stress is moderate and change to 3 in column 1 when the stress is low
d. In the
Toolbar
, click
Analyze
, select
Compare Means
, and then select
One-Way ANOVA.
e. Select
Fat
and then click
Arrow
to send it over to the
Dependent List
box.
f. Select
Stress
and then click
Arrow
to send it over to the
Factor
box.
g. Click
OK
and copy and paste the output below.
Problem Set 4.3: One-way ANOVA in Excel
Criterion:
Calculate an ANOVA in Excel.
Instructions:
Use the data from Problem Set 4.3 to complete the following steps:
a. Open
Excel
to an empty sheet.
b. Enter the data from
Problem Set 4.3.
c. In
Row 1
, enter High in cell A1, Moderate in cell B1, and Low in cell B1.
d. In the toolbar, click
Data Analysis
, select
Anova: Single Factor,
and click
OK.
e. In
Input Range
: $A$1:$C$6, put a check next to
Lab.
I need this done ASAP, You have to have SPSS Software on your comput.docxanthonybrooks84958
I need this done ASAP, You have to have SPSS Software on your computer. Please do not request to do the assignment if you don't have the software or if you do not have the understanding to get this assignment complete.
Assignment 2: Tests of Significance
Throughout this assignment you will review mock studies.
You will needs to follow the directions outlined in the section using SPSS and decide whether there is significance between the variables.
You will need to list the five steps of hypothesis testing (as covered in the lesson for Week 6) to see how
every
question should be formatted.
You will complete all of the problems.
Be sure to cut and past the appropriate test result boxes from SPSS under each problem and explain what you will do with your research hypotheses.
All calculations should be coming from your SPSS
.
You will need to submit the SPSS output file to get credit for this assignment.
This file will save as a .spv file and will need to be in a single file.
In other words, you are not allowed to submit more than one output file for this assignment.
The five steps of hypothesis testing when using SPSS are as follows:
State your research hypothesis (H
1
) and null hypothesis (H
0
).
Identify your significance level (.05 or .01)
Conduct your analysis using SPSS.
Look for the valid score for comparison.
This score is usually under ‘Sig 2-tail’ or ‘Sig. 2’.
We will call this “p”.
Compare the two and apply the following rule:
If “p” is < or = significance level, than you reject the null.
Be sure to explain to the reader what this means in regards to your study.
(Ex: will you recommend counseling services?)
* Be sure that your answers are clearly distinguishable.
Perhaps you bold your font or use a different color.
This assignment is due no later than Sunday of Week 6 by 11:55 pm ET.
Save the file in the following format: [your last name_SOCI332_A2].
The file must be a word file.
t Tests
t Test for a Single Sample (20 points)
Open SPSS
Enter the number of activities of daily living performed by the depressed clients studied in #1 in the Data View window.
In the Variable View window, change the variable name to “ADL” and set the decimals to zero.
Click Analyze
Compare Means
One-Sample T test
the arrow to move “ADL” to the Variable(s) window.
Enter the population mean (17) in the “Test Value” box.
Click OK.
Researches are interested in whether depressed people undergoing group therapy will perform a different number of activities of daily living after group therapy. The researchers have randomly selected 12 depressed clients to undergo a 6-week group therapy program.
Use the five steps of hypothesis testing to determine whether the average number of activities of daily living (shown below) obtained after therapy is significantly different from a mean number of activities of 17 that is typical for depressed people. (Clearly indicate each step).
Test the difference at the .05 level of significance a.
The document provides instructions for launching and using the statistical software SPSS. It discusses finding the SPSS icon on the computer and launching the program. Once SPSS is open, the user can start a new data file or open an existing one. Basic steps for using SPSS are outlined, including entering data, defining variables, testing for normality, statistical analysis, and interpreting results. Specific functions and menus in SPSS are demonstrated for descriptive statistics, normality testing, and t-tests.
This document discusses how spreadsheets like Microsoft Excel and Google Sheets can be used to account for expenses and determine if a product's estimated sale price will be profitable. It provides an example of using Excel to calculate the total cost of ingredients for a milk tea product and compare it to the estimated sale price to see if it will earn a profit. The document also demonstrates how Excel formulas like SUM, subtraction, COUNTIF, and AVERAGEIF can be used to analyze survey data about a product to help evaluate its potential success in the target market.
1) The document provides instructions for teachers on using the PowerTeacher gradebook system to take attendance, enter assignments and scores, set up categories and weighting for calculating final grades, and generate reports.
2) Key functions covered include taking attendance, accessing student information and alerts, adding and editing categories and assignments, setting up weighting for calculating term and final grades, and using the grade setup tool for determining final grade calculations.
3) Teachers are instructed to set weighting for elementary schools with terms weighted at 50-50 and categories weighted within terms, and for middle/high schools with terms and categories weighted differently depending on grade level.
This document provides an introduction to correlation and regression analysis. It defines correlation as a measure of the association between two variables and regression as using one variable to predict another. The key aspects covered are:
- Calculating correlation using Pearson's correlation coefficient r to measure the strength and direction of association between variables.
- Performing simple linear regression to find the "line of best fit" to predict a dependent variable from an independent variable.
- Using a TI-83 calculator to graphically display scatter plots of data and calculate the regression equation and correlation coefficient.
This document provides an introduction and overview of how to use the statistical software package SPSS. It discusses getting started with SPSS, opening and viewing data files, coding variables, and performing basic descriptive statistics. Specific tasks covered include entering and labeling variables, assigning value labels, handling missing data, generating frequency tables and graphs like histograms and box plots, recoding variables, and using the Compute function to calculate new variables.
This document provides instructions for first-time users of the EGP gradebook program on how to set up classes, categories, assignments, input grades, and print reports. It covers how to connect to the server, open an existing gradebook, customize class and assignment options, enter scores and grades, filter views, rename classes, and combine grades across terms.
This document provides an introduction and guidelines for applied statistics and statistical methods. It discusses topics like data handling, file handling, and graphs. For data handling, it describes how to recode variables into different groups and compute new variables using algebraic functions of existing variables. For file handling, it discusses how to select only certain cases, such as males, and split a file into groups. Finally, it demonstrates how to create histograms and boxplots to explore the distribution of variable scores and identify any outliers in the data.
We would like to introduce sampling software which costs just 10 USD. Sampling is statistical software designed to calculate sampling computation easily such as stratified sampling, cluster sampling, sampling with varying probability and etc. You can download free 7 times running trial license here:
http://www.sampling-software.com
This document provides an overview of SPSS and how to perform basic analyses in it. It discusses the four main windows in SPSS: the data editor, output viewer, syntax editor, and script window. It then covers how to open and manage data files, define variables, sort and transform data. The document concludes by demonstrating how to conduct frequency analyses, descriptive statistics, linear regression analyses, and plot regression lines in SPSS through both the graphical user interface and syntax editor.
This document provides instructions for performing various statistical analyses and data management tasks in SPSS, including sorting data, selecting cases, splitting files, merging files, visual binning, frequencies analysis, descriptive statistics, cross tabulation and chi-square tests, independent samples t-tests, and one-way ANOVA. The document is authored by trainers from the Department of Applied Statistics at the University of Rwanda and dated December 6, 2014.
The document discusses the steps for conducting a response surface methodology (RSM) experiment using central composite design (CCD). It involves determining independent and dependent variables, selecting an appropriate CCD, conducting the experiment runs according to the design, analyzing the data using statistical methods to develop a mathematical model and check its adequacy, and using the model to optimize responses. Key aspects of RSM and CCD covered include developing the design, analyzing results through ANOVA and regression, and checking model validity.
This document discusses linear programming (LP), including:
1. LP is a quantitative tool used by managers to obtain optimal solutions to problems with restrictions or limitations. Applications include production scheduling and facility location planning.
2. An LP model has an objective function that represents profit/cost, decision variables representing amounts, constraints limiting alternatives, and parameters.
3. Excel's Solver tool can be used to solve LP problems by specifying the target cell, decision variable cells, constraints, and solving to find optimal values.
Software packages for statistical analysis - SPSSANAND BALAJI
This document provides an overview of the Statistical Package for Social Sciences (SPSS). It discusses what SPSS is, how to define and enter variables, and the four main windows in SPSS including the data editor, output viewer, syntax editor, and script window. Basic functions like frequencies analysis, descriptives, and linear regression are also introduced.
De vry math 399 all ilabs latest 2016 novemberlenasour
This document provides information about obtaining assistance with coursework from an online service called ACEHOMEWORK.NET. It lists various courses and assignments they can help with, such as accounting, marketing, finance, economics, mathematics, statistics, programming, and more. It emphasizes they can help students get an A grade and provide original, plagiarism-free work by the deadline. Contact information is provided to obtain more details and pricing information.
This document provides instructions for conducting a linear regression analysis in SPSS using one predictor and one criterion variable. It demonstrates how to perform the analysis using data on fertility rates and infant mortality rates in different countries. Key steps include selecting the variables, running the analysis, and interpreting output sections like the model summary, ANOVA table, coefficients, and interactive scatterplot with a regression line showing the relationship between the variables.
This document provides an overview of the Statistical Package for Social Sciences (SPSS) software. It describes the main components and windows in SPSS, including the data window, variable view window, output window, and chart editor window. It also outlines several statistical techniques that can be performed in SPSS, such as descriptive statistics, correlations, t-tests, and chi-square tests of independence. SPSS is a tool that allows users to manage and analyze data, as well as generate graphs and conduct a wide range of statistical procedures.
This document provides an introduction to using the Excel Solver tool to solve optimization problems. It explains how to enable the Solver add-in, the four main steps to solving a linear problem using Solver: 1) organizing the problem information, 2) setting up the problem in a spreadsheet, 3) running Solver, and 4) interpreting the results. It also includes an example problem about maximizing profits from manufacturing different products with limited resources, and shows how to set up and solve this problem using Solver.
Using excel to convert raw score to z scoreSandra Nicks
The document provides instructions for converting raw scores to z-scores in Excel. It describes entering raw scores in a column, using the Data Analysis tool to calculate the mean and standard deviation of the raw scores. It then explains the formula to convert each raw score to a z-score by taking the raw score minus the mean and dividing by the standard deviation. The user is guided to enter this formula in a cell and copy it down to convert all raw scores to z-scores.
ANOVA, Chi-Square Tests, and RegressionComplete the followin.docxamrit47
ANOVA, Chi-Square Tests, and Regression
Complete the following problems within this Word document. Do not submit other files. Show your work for problem sets that require calculations. Ensure that your answer to each problem is clearly visible. You may want to highlight your answer or use a different type color to set it apart.
ANOVA
Problem Set 4.1: Critical Value
Criterion:
Explain the relationship between
k
and power based on calculated
k
values.
Instructions:
Read the following and answer the questions.
Work through the following and write down what you see in the
F-
table. This will help familiarize you with the table.
The
F-
table: The degrees of freedom for the numerator (
k
− 1) are across the columns; the degrees of freedom for the denominator (
N
−
k
) are across the rows in the table. A separate table is included for a .05 and .01 level of significance.
Increasing the levels of the independent variable (
k
):
Suppose we have a sample size of 24 participants (
N
= 24). Record the critical values given the following values for
k
:
.05
.01
k
= 2
k
= 4
k
= 6
k
= 8
___
___
___
___
___
___
___
___
As
k
increases (from 1 to 8), does the critical value increase or decrease? Based on your answer, explain how
k
is related to power.
Problem Set 4.2: One-way ANOVA in SPSS
Criterion:
Calculate an ANOVA in SPSS.
Data:
The following is the amount of fat (in grams) consumed in a buffet-style lunch among professional bodybuilders under conditions of high, moderate, and low stress:
Stress Levels
High
Moderate
Low
10
9
9
7
4
4
8
7
6
12
6
5
6
8
7
Instructions:
Complete the following steps:
a. Open SPSS and open a
New DataSet
.
b. Click the
Variable View
tab at the bottom and enter
Stress
and enter
Fat
as the variables. Click the
Values
box for the
Stress
row and define 1 as high, 2 as medium, and 3 as low.
c. Enter the data. For example, type 1 in row 1 under
Stress
and type 10 in row 1 under
Fat
. Continue typing in all the data. Please remember to change to 2 in column 1 when the stress is moderate and change to 3 in column 1 when the stress is low
d. In the
Toolbar
, click
Analyze
, select
Compare Means
, and then select
One-Way ANOVA.
e. Select
Fat
and then click
Arrow
to send it over to the
Dependent List
box.
f. Select
Stress
and then click
Arrow
to send it over to the
Factor
box.
g. Click
OK
and copy and paste the output below.
Problem Set 4.3: One-way ANOVA in Excel
Criterion:
Calculate an ANOVA in Excel.
Instructions:
Use the data from Problem Set 4.3 to complete the following steps:
a. Open
Excel
to an empty sheet.
b. Enter the data from
Problem Set 4.3.
c. In
Row 1
, enter High in cell A1, Moderate in cell B1, and Low in cell B1.
d. In the toolbar, click
Data Analysis
, select
Anova: Single Factor,
and click
OK.
e. In
Input Range
: $A$1:$C$6, put a check next to
Lab.
I need this done ASAP, You have to have SPSS Software on your comput.docxanthonybrooks84958
I need this done ASAP, You have to have SPSS Software on your computer. Please do not request to do the assignment if you don't have the software or if you do not have the understanding to get this assignment complete.
Assignment 2: Tests of Significance
Throughout this assignment you will review mock studies.
You will needs to follow the directions outlined in the section using SPSS and decide whether there is significance between the variables.
You will need to list the five steps of hypothesis testing (as covered in the lesson for Week 6) to see how
every
question should be formatted.
You will complete all of the problems.
Be sure to cut and past the appropriate test result boxes from SPSS under each problem and explain what you will do with your research hypotheses.
All calculations should be coming from your SPSS
.
You will need to submit the SPSS output file to get credit for this assignment.
This file will save as a .spv file and will need to be in a single file.
In other words, you are not allowed to submit more than one output file for this assignment.
The five steps of hypothesis testing when using SPSS are as follows:
State your research hypothesis (H
1
) and null hypothesis (H
0
).
Identify your significance level (.05 or .01)
Conduct your analysis using SPSS.
Look for the valid score for comparison.
This score is usually under ‘Sig 2-tail’ or ‘Sig. 2’.
We will call this “p”.
Compare the two and apply the following rule:
If “p” is < or = significance level, than you reject the null.
Be sure to explain to the reader what this means in regards to your study.
(Ex: will you recommend counseling services?)
* Be sure that your answers are clearly distinguishable.
Perhaps you bold your font or use a different color.
This assignment is due no later than Sunday of Week 6 by 11:55 pm ET.
Save the file in the following format: [your last name_SOCI332_A2].
The file must be a word file.
t Tests
t Test for a Single Sample (20 points)
Open SPSS
Enter the number of activities of daily living performed by the depressed clients studied in #1 in the Data View window.
In the Variable View window, change the variable name to “ADL” and set the decimals to zero.
Click Analyze
Compare Means
One-Sample T test
the arrow to move “ADL” to the Variable(s) window.
Enter the population mean (17) in the “Test Value” box.
Click OK.
Researches are interested in whether depressed people undergoing group therapy will perform a different number of activities of daily living after group therapy. The researchers have randomly selected 12 depressed clients to undergo a 6-week group therapy program.
Use the five steps of hypothesis testing to determine whether the average number of activities of daily living (shown below) obtained after therapy is significantly different from a mean number of activities of 17 that is typical for depressed people. (Clearly indicate each step).
Test the difference at the .05 level of significance a.
The document provides instructions for launching and using the statistical software SPSS. It discusses finding the SPSS icon on the computer and launching the program. Once SPSS is open, the user can start a new data file or open an existing one. Basic steps for using SPSS are outlined, including entering data, defining variables, testing for normality, statistical analysis, and interpreting results. Specific functions and menus in SPSS are demonstrated for descriptive statistics, normality testing, and t-tests.
This document discusses how spreadsheets like Microsoft Excel and Google Sheets can be used to account for expenses and determine if a product's estimated sale price will be profitable. It provides an example of using Excel to calculate the total cost of ingredients for a milk tea product and compare it to the estimated sale price to see if it will earn a profit. The document also demonstrates how Excel formulas like SUM, subtraction, COUNTIF, and AVERAGEIF can be used to analyze survey data about a product to help evaluate its potential success in the target market.
1) The document provides instructions for teachers on using the PowerTeacher gradebook system to take attendance, enter assignments and scores, set up categories and weighting for calculating final grades, and generate reports.
2) Key functions covered include taking attendance, accessing student information and alerts, adding and editing categories and assignments, setting up weighting for calculating term and final grades, and using the grade setup tool for determining final grade calculations.
3) Teachers are instructed to set weighting for elementary schools with terms weighted at 50-50 and categories weighted within terms, and for middle/high schools with terms and categories weighted differently depending on grade level.
This document provides an introduction to correlation and regression analysis. It defines correlation as a measure of the association between two variables and regression as using one variable to predict another. The key aspects covered are:
- Calculating correlation using Pearson's correlation coefficient r to measure the strength and direction of association between variables.
- Performing simple linear regression to find the "line of best fit" to predict a dependent variable from an independent variable.
- Using a TI-83 calculator to graphically display scatter plots of data and calculate the regression equation and correlation coefficient.
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This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
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Correlation Between SAT and GWA
1. Workshop on Correlation:
Scholastic Admission Test (SAT) and Graded Weighted Average (GWA)
Seminar – Workshop 1
In these exercises, you will standardize the raw scores of the target group; determine the
correlation between their percentile ranks and GWA; and compare groups using the sex
variable in the correlation. The Scatterplot will also be use in the correlation output.
A. Opening the Spreadsheets Containing the Data.
1. Open the WORKSHOP_DATA folder at the Desktop or with the use of the
Windows Explorer look for the WORKSHOP_DATA folder in the Drive D: and
open it.
2. Double – click TARGET.XLS in the WORKSHOP_DATA folder to open it.
3. Save the spreadsheet file with another name File – Save – As “EXER05.XLS”.
The purpose of doing this is to reserve the original file. In case, you make mistakes in
the process, you can reopen the original file.
B. In this part, the Z–scores will be derived from the raw scores of the target group using
the mean (42.91) and standard deviation (12.264) of normative group composed of 961
examinees.
Z–Scores are a transformation of individual raw scores into a standard form, where
the transformation is based on knowledge about the standardization sample’s mean
and standard deviation.
The formula for computing Z–scores is the individual raw score (X) minus the mean of
the scores obtained by the standardization sample (M), divided by the standard
deviation of scores obtained by the standardization sample (sd). Z–scores have a mean
of 0 and a standard deviation of 1. (BROCK, 2012).
4. Currently, ‘EXER05.XLS is open. At the cell J2, type “ZCORES” and press
<ENTER>. Click the cell J3, type ‘=standardize(E3,42.91,12.264)’ and press
<ENTER>. The cell E3 is the location of the first raw score at column RSCORE. The
value 42.91 is the mean of the 961 examinees, and 12.264 is the Standard Deviation.
5. Click the cell J3 again and at the lower right corner of this cell, double – click the
small square to apply the same formula to the rest of the items.
2. Workshop on Correlation:
Scholastic Admission Test (SAT) and Graded Weighted Average (GWA)
Seminar – Workshop 2
C. Exporting the Data from EXERO5.XLS to IBM SPSS
6. Click File Save As. The File – Save As Dialog Appears. In the ‘Save as Type’
section, select ‘Text (Tab delimited)’. Filename is still ‘EXER05’. Then click Save.
7. Click the Yes button.
3. Workshop on Correlation:
Scholastic Admission Test (SAT) and Graded Weighted Average (GWA)
Seminar – Workshop 3
8. Click File Exit and then click No button.
9. Open IBM SPSS 16.0 or different version. If the opening window appears, just click
the Cancel button.
10.Click File Read Text Data.
4. Workshop on Correlation:
Scholastic Admission Test (SAT) and Graded Weighted Average (GWA)
Seminar – Workshop 4
11. The Open Data dialog box appears. Go the WORKSHOP_DATA folder and
look for ‘EXER05.TXT’ and then click the Open button.
12.The Text Import Wizard appears. Click Next in ‘Step 1 of 6’ window.
13.Click ‘Yes’ to answer the question “Are the variable names included at the top of
your file?. Then, click next.
5. Workshop on Correlation:
Scholastic Admission Test (SAT) and Graded Weighted Average (GWA)
Seminar – Workshop 5
14. Click Next on ‘Step 3 of 6’ to ‘Step 5 of 6’. Finally, click ‘Finish’ on ‘Step 6 of 6’.
15. Save your file as ‘EXER05.SAV.
NOTE: You can skip sections D to F and go directly to section G. Section G focuses
on the correlation between RSCORE and GWA.
D. In this part, you will compute the Percentile Ranks of the target group based on their
Z–scores.
16.Click TRANSFORM > COMPUTE. Type “PRCTLRANK” in the TARGET
VARIABLE box. Select “CDF & NONCENTRAL CDF” from the FUNCTION
GROUP box. Select “CDF.NORMAL” function from the FUNCTIONS AND
SPECIAL VARIABLES box. Click the up arrow , select “ZSCORES” from the
variable box and click the right arrow to replace the first “?” inside the function
CDF.NORMAL(?,?,?). Type “0” for the second “?” represents the mean and type “1”
for the third “?” representing standard deviation. Click OK.
The resulting screen is shown.
CDF.NORMAL(ZSCORES,0,1)
6. Workshop on Correlation:
Scholastic Admission Test (SAT) and Graded Weighted Average (GWA)
Seminar – Workshop 6
17.In creating the column PRANK100, click TRANSFORM > COMPUTE VARIABLE.
Type “PRANK100” in the TARGET VARIABLE box. Select “PRCNTLRANK”
from the variables box and click the right arrow , to move it to NUMERIC
EXPRESSION box. Complete the numeric expression by type “* 100”. The purpose
of this expression is to convert the percentile ranks to the nearest ones. Then click OK.
PRCNTLRANK * 100
7. Workshop on Correlation:
Scholastic Admission Test (SAT) and Graded Weighted Average (GWA)
Seminar – Workshop 7
E. In this part, you will be creating another column similar to stanine but have five scales
only. The table below will be used.
FIVE
SCALES
VERBAL
DESCRIPTION
RANGES OF
PERCENTILE
RANKS
PERCENTAGES
STANINE
SCALE
5 Superior (4%) 96 and above 4% 9
89 – 95 7% 8
4
Above Average
(19%) 77 – 88 12% 7
60 – 76 17% 6
40 – 59 20% 5
3 Average (54%)
23 – 39 17% 4
11 – 22 12% 3
2
Below Average
(19%) 4 – 10 7% 2
1 Low (4%) Below 4 4% 1
18.In the menu, click TRANSFORM > XY RECODE INTO DIFFERENT
VARIABLES. Select PRANK100 from the variable box at the left and move it into
the INPUT VARIABLE OUTPUT VARIABLE box by clicking the right arrow .
Type “FSCALE” into the NAME box and LABEL box at the OUTPUT VARIABLE
section. Then click the CHANGE button.
8. Workshop on Correlation:
Scholastic Admission Test (SAT) and Graded Weighted Average (GWA)
Seminar – Workshop 8
Then click the OLD AND NEW VALUES button.
Perform the following steps in entering the different ranges of percentile ranks for
each scale:
Type 0 in the RANGE box and 3.9 in the THROUGH box. Type 1 in the
VALUE under the NEW VALUE section and then click the ADD button in the
OLD NEW section.
Type 4 in the RANGE box and 22.4 in the THROUGH box. Type 2 in the
VALUE under the NEW VALUE section and then click the ADD button in the
OLD NEW section.
Type 22.5 in the RANGE box and 76.4 in the THROUGH box. Type 3 in the
VALUE under the NEW VALUE section and then click the ADD button in the
OLD NEW section.
Type 76.5 in the RANGE box and 95.4 in the THROUGH box. Type 4 in the
VALUE under the NEW VALUE section and then click the ADD button in the
OLD NEW section.
Type 95.5 in the RANGE box and 100 in the THROUGH box. Type 5 in the
VALUE under the NEW VALUE section and then click the ADD button in the
OLD NEW section.
The resulting screen is shown.
Click the CONTINUE button. Then OK. The column FSCALE is added.
9. Workshop on Correlation:
Scholastic Admission Test (SAT) and Graded Weighted Average (GWA)
Seminar – Workshop 9
F. In this part, you will be creating the histogram of the column FSCALE.
19.At the menu, click GRAPHS > LEGACY DIALOGS > HISTOGRAM. Select
FSCALE from the variables box, click the right arrow. Click DISPLAY NORMAL
CURVE. Click OK.
The resulting histogram is shown.
Next, save your file.
G. Correlation Between RSCORES and GWA using Pearson Product Moment
Correlation (PPMC).
Correlation refers to any of a broad class of statistical relationships involving between
two random variables or two sets of data (Correlation and Dependence, 2012). One
way to express correlation is by the use of Pearson Product Moment Correlation
coefficient.
The Pearson Product – Moment Correlation Coefficient (r), or correlation coefficient
for short is a measure of the degree of linear relationship between two variables,
usually labeled X and Y. The correlation coefficient may take on any value between
plus and minus one. (Stockburger, 1996)
10. Workshop on Correlation:
Scholastic Admission Test (SAT) and Graded Weighted Average (GWA)
Seminar – Workshop 10
20. At the ANALYZE menu, click CORRELATE > BIVARIATE. Select RSCORE and
GRADE and move to the VARIABLES box using the right–arrow . By default,
PEARSON, TWO–TAILED, and FLAG SIGNIFICANT CORRELATIONS
are already selected.
Click OPTIONS button, check MEANS AND STANDARD DEVIATIONS. Then
click CONTINUE.
Next, click OK.
11. Workshop on Correlation:
Scholastic Admission Test (SAT) and Graded Weighted Average (GWA)
Seminar – Workshop 11
21. Save the viewer file as ‘EXER0501”. These are the information you can see in the
output.
The correlation coefficient between
RSCORE and GRADE is 0.260, which
is a weak correlation..
The p–value is found in the row of Sig
(2–tailed). The p–value is 0.009 which
is lesser than the alpha value of 0.05,
and marginal to 0.01.
This means that there is a significant
relationship between SEX and
RSCORE.
H. In this part, you will be using the Scatterplot graph.
22.At the GRAPHS menu, click LEGACY DIALOGS > SCATTER/DOT…. and then
select SIMPLE SCATTER and click the DEFINE button.
12. Workshop on Correlation:
Scholastic Admission Test (SAT) and Graded Weighted Average (GWA)
Seminar – Workshop 12
The DEFINE SIMPLE SCATTER PLOT dialog.
Move the RSCORE variable to X–AXIS, and GRADE variable to Y–AXIS and then
click OK.
The resulting graph is shown.
To add a FIT LINE through the scattered dots, double–click on
the graph to display the CHART EDITOR. At the CHART
EDITOR click the ADD FIT LINE AT TOTAL icon.
By the time you click the ADD FIT LINE AT TOTAL icon, its
PROPERTIES dialog will appear. Simply click the CLOSE
button of the PROPERTIES dialog, then click FILE > CLOSE
the CHART EDITOR to close.
The resulting graph is shown.
13. Workshop on Correlation:
Scholastic Admission Test (SAT) and Graded Weighted Average (GWA)
Seminar – Workshop 13
The Line of Best Fit or Fit Line is inclined
upward to the right.
Finally, save your file.
I. In this part, you will perform the Subgroup Correlations by Sex
23.You need to get SPSS to calculate the correlation between RSCORE and GWA
separately for males represented as 1 and females represented as 2. The easiest way
to do this is to split our data file by sex.
In the main menu, select DATA > SPLIT FILE. Select ORGANIZE OUTPUT BY
GROUPS and GROUPS BASED ON SEX. This means that any analyses you specify will
be run separately for males and females. Then, click Ok.
Notice that the order of the data files has been changed. It is now sorted by SEX,
with males at the top of the file.
14. Workshop on Correlation:
Scholastic Admission Test (SAT) and Graded Weighted Average (GWA)
Seminar – Workshop 14
Now, select ANALYZE > CORRELATION > BIVARIATE. The same variables
and options you selected last time are still in the dialog box.
Click OPTIONS button, check MEANS AND STANDARD DEVIATIONS.
Then click CONTINUE. Then, click OK.
Take a moment to check to see for you. The output follow broken down by males
(1) and females (2).
Display results for Males:
15. Workshop on Correlation:
Scholastic Admission Test (SAT) and Graded Weighted Average (GWA)
Seminar – Workshop 15
Display results for Females:
J. Scatterplots of Data by Subgroups
In this part, you will create a more complicated scatterplot that illustrates the pattern
of correlation for males and females on one graph.
24.First, you need to turn off SPLIT FILE. Select DATA > SPLIT FILE from the main
menu. Then select ANALYZE ALL CASES, do not compare groups and click OK.
Now, you can proceed.
The SPLIT FILE is turn off.
16. Workshop on Correlation:
Scholastic Admission Test (SAT) and Graded Weighted Average (GWA)
Seminar – Workshop 16
25.Select GRAPHS > LEGACY > SCATTER. Then, select SIMPLE and click DEFINE.
Select GRADE as the Y Axis and
RSCORE as the X Axis. Then,
select SEX for SET MARKERS
BY. This means SPSS will
distinguish the males dots from the
female dots on the graph. Then,
click OK.
To distinguish clearly the dots from the males and females, you will edit the graph.
Double click the graph to activate the CHART EDITOR.
17. Workshop on Correlation:
Scholastic Admission Test (SAT) and Graded Weighted Average (GWA)
Seminar – Workshop 17
Then double click on one of the female (2) dots. SPSS will highlight all the female
dots. Then click the MARKER menu.
Select the CIRCLE under MARKER TYPE and chose a Fill color. Then click
APPLY. Then CLOSE. You can do the same procedure for the male (1) dots by
double clicking on the male dot as a start. Choose a different color for the male.
26. You would like to alter our graph to include the line of best fit for the male and
female groups. Under ELEMENTS, select FIT LINE at SUBGROUPS. Then
select LINEAR and click APPLY and CLOSE. Lastly, in the CHART EDITOR,
click FILE > CLOSE.
18. Workshop on Correlation:
Scholastic Admission Test (SAT) and Graded Weighted Average (GWA)
Seminar – Workshop 18
The resulting graph follows.
Finally, save your files.
References:
Brock, S. E. Descriptive Statistics and Psychological Testing. California State
University, Sacramento. Retrieved April 05, 2012.
Correlation. In http://www.uvm.edu. Retrieved August 21, 2012, from
http://www.uvm.edu/~dhowell/fundamentals7/SPSSManual/SPSSLongerManual
/SPSSChapter5.pdf
Correlation and Dependence (2012, March 16). In http://www.wikipedia.org.
Retrieved August 21, 2012, from http://www.wikipedia/correlation_and_
dependence.html
Cohen, R. J., & Swerdlik, M. E. (2005). Psychological Testing and Assessment:
An Introduction to Tests and Measurement. (6th
Edition.) McGraw–Hill.
Stockburger, D. W. (1996). Introductory Statistics: Concepts, Models, and
Applications. Atomic Dog Publishing. Missouri State University
Zucker, S. (2003, December). Fundamentals of Standardized Testing. Retrieved
April 06, 2012 from http://www.hemweb.com.