This document provides a detailed guide on performing regression analysis in SPSS. It covers the hypothesis, variables entered/removed, model summary, ANOVA, and coefficients. The document also includes step-by-step instructions and examples. Perfect for students studying statistics or data analysis.
Contribute Materials
Your contribution can guide someone’s learning journey. Share your
documents today.
SPSS
Secure Best Marks with AI Grader
Need help grading? Try our AI Grader for instant feedback on your assignments.
TABLE OF CONTENTS 12.2..................................................................................................................................................3 Regression....................................................................................................................................3 12.4..................................................................................................................................................6 A..................................................................................................................................................6 12.12................................................................................................................................................7 A. Implication of experimental technique to be used in analyzing the outcomes.......................7 B. Analyzing the means of variables...........................................................................................7 12.14................................................................................................................................................8 A. Identification of the treatment to be followed........................................................................8 12.22................................................................................................................................................8 SAS..............................................................................................................................................9 12.24..............................................................................................................................................11 12.26..............................................................................................................................................12 Regression..................................................................................................................................12 12.36..............................................................................................................................................14 Regression..................................................................................................................................14 REFERENCES..............................................................................................................................16
12.2 Regression Hypothesis: Null hypothesis(H0): There is no statistically significant difference in the mean values of dependent variable round and independent variable power Alternative hypothesis(H1): There is a statistically significant difference in the mean values of dependent variable round and independent variable power Variables Entered/Removeda ModelVariables Entered Variables Removed Method 1POWERb.Enter a. Dependent Variable: ROUND b. All requested variables entered. Model Summaryb ModelRR SquareAdjusted R Square Std. Error of the Estimate Durbin-Watson 1.234a.055.0231.123.214 a. Predictors: (Constant), POWER b. Dependent Variable: ROUND ANOVAa ModelSum of SquaresdfMean SquareFSig. 1 Regression2.18112.1811.730.198b Residual37.819301.261 Total40.00031 a. Dependent Variable: ROUND b. Predictors: (Constant), POWER Coefficientsa
12.4 A. Hypothesis Null hypothesis(H0): There is no statistically significant difference in the mean values of dependent variables (Overrun & score) and factor variable power Alternative hypothesis(H1): There is a statistically significant difference in the mean values of dependent variables (Overrun & score) and factor variable power
One way ANOVA ANOVA Sum of SquaresdfMean SquareFSig. OVERRUN Between Groups9669.95823420.433.. Within Groups.0000. Total9669.95823 SCORE Between Groups1.17128.042.365.938 Within Groups.3443.115 Total1.51531 12.12 A. Implication of experimental technique to be used in analyzing the outcomes T-test: To identify the statistical differences between group and score there will be implication of t-test techniques which will bring the adequate outcomes. Moreover, in relation with analyzing the independent T-test outcomes on which it can be said that there have been implication of two or more variables which will be categories as per denoting them groups. Thus, it will be helpful approach which in turn used for comparing the mean value of two independent groups on which the statistical determination of the statistical evidences based on population means are significantly different. B. Analyzing the means of variables T-test: Group Statistics GROUPNMeanStd. DeviationStd. Error Mean SCORE111.4400.17053.05142 211.2655.15260.04601
Secure Best Marks with AI Grader
Need help grading? Try our AI Grader for instant feedback on your assignments.
Independent Samples Test Levene's Test for Equality of Variances t-test for Equality of Means FSig.tdfSig. (2- tailed) Mean Difference Std. Error Difference 95% Confidence Interval of the Difference LowerUpper SCORE Equal variances assumed .020.8882.53020.020.17455.06900.03062.31847 Equal variances not assumed 2.53019.758.020.17455.06900.03051.31858 12.14 A. Identification of the treatment to be followed. In relation with ascertaining the outcomes there have been use of various outcomes. In relation with analyzing the outcomes as considering population there is needed to have adequate determination of the facts (Annotated output,2018). Thus, considering descriptive analysis, one way ANOVAs and various techniques which will be helpful in determining outcomes. 12.22 One way ANOVA Null hypothesis (H0): There is no statistically significant difference in the mean values of dependent variable GROUP and other independent variable SOCRE. Alternative hypothesis (H1):There is a statistically significant difference in the mean values of variable GROUP and other independent variable SOCRE. ANOVA GROUP
Sum of SquaresdfMean SquareFSig. Between Groups42.833341.260.932.593 Within Groups12.16791.352 Total55.00043 SAS Frequencies Statistics GROUPSCORE NValid4444 Missing11 Mean2.50.2923 Std. Error of Mean.170.03396 Median2.50.3150 Mode1a.20a Std. Deviation1.131.22528 Variance1.279.051 Skewness.000-.834 Std. Error of Skewness.357.357 Kurtosis-1.378.590 Std. Error of Kurtosis.702.702 Range3.97 Minimum1-.35 Maximum4.62 Percentiles 251.25.1600 502.50.3150 753.75.4700 a. Multiple modes exist. The smallest value is shown Frequency Table GROUP
.4012.22.361.4 .4212.22.363.6 .4324.44.568.2 .4412.22.370.5 .4612.22.372.7 .4724.44.577.3 .5036.76.884.1 .5112.22.386.4 .5212.22.388.6 .5312.22.390.9 .5824.44.595.5 .6112.22.397.7 .6212.22.3100.0 Total4497.8100.0 MissingSystem12.2 Total45100.0 Descriptive Statistics NMinimumMaximumMeanStd. Deviation GROUP44142.501.131 SCORE44-.35.62.2923.22528 Valid N (listwise)44 12.24 One way ANOVA Hypothesis: Null hypothesis (H0): There is no statistically significant difference in the mean values of dependent variable abstract and other independent variable factor (day). Alternative hypothesis (H1): There is a statistically significant difference in the mean values of variable abstract and other independent variable factor (day).
ANOVA absrate Sum of SquaresdfMean SquareFSig. Between Groups94.199423.550.989.424 Within Groups952.1694023.804 Total1046.36844 12.26 Regression Hypothesis Null hypothesis (H0): There is no statistically significant difference in the mean values of power and other independent variables (boxer & round). Alternative hypothesis (H1): There is a statistically significant difference in the mean values of power and other independent variables (boxer & round). Descriptive Statistics MeanStd. DeviationN POWER1253.3471.69032 BOXER4.502.32832 ROUND2.501.13632 Correlations POWERBOXERROUND Pearson Correlation POWER1.000.204.234 BOXER.2041.000.000 ROUND.234.0001.000 Sig. (1-tailed) POWER..132.099 BOXER.132..500 ROUND.099.500.
N POWER323232 BOXER323232 ROUND323232 Variables Entered/Removeda ModelVariables EnteredVariables Removed Method 1ROUND, BOXERb.Enter a. Dependent Variable: POWER b. All requested variables entered. Model Summary ModelRR Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1df2Sig. F Change 1.310a.096.03470.470.0961.541229.231 a. Predictors: (Constant), ROUND, BOXER ANOVAa ModelSum of SquaresdfMean SquareFSig. 1 Regression15306.62727653.3131.541.231b Residual144016.592294966.089 Total159323.21931 a. Dependent Variable: POWER b. Predictors: (Constant), ROUND, BOXER Coefficientsa ModelUnstandardized Coefficients Standardized Coefficients tSig.95.0% Confidence Interval for B BStd. ErrorBetaLower BoundUpper Bound 1(Constant)1188.25439.11230.381.0001108.2621268.247 BOXER6.2775.437.2041.154.258-4.84317.397
Secure Best Marks with AI Grader
Need help grading? Try our AI Grader for instant feedback on your assignments.
ROUND14.73811.142.2341.323.196-8.05137.526 a. Dependent Variable: POWER Regression analysis Null hypothesis (H0): There is no statistically significant difference in the mean values of power and other independent variables (boxer & round). Alternative hypothesis (H1):There is a statistically significant difference in the mean values of power and other independent variables (boxer & round). 12.36 Regression Descriptive Statistics MeanStd. DeviationN OVERRUN510.5420.50424 HOUSE2.501.14224 WT1.50.51124 Correlations OVERRUNHOUSEWT Pearson Correlation OVERRUN1.000.762.139 HOUSE.7621.000.000 WT.139.0001.000 Sig. (1-tailed) OVERRUN..000.258 HOUSE.000..500 WT.258.500. N OVERRUN242424 HOUSE242424 WT242424
Variables Entered/Removeda ModelVariables EnteredVariables Removed Method 1WT, HOUSEb.Enter a. Dependent Variable: OVERRUN b. All requested variables entered. Model Summary ModelRR Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1df2Sig. F Change 1.775a.600.56213.568.60015.764221.000 a. Predictors: (Constant), WT, HOUSE ANOVAa ModelSum of SquaresdfMean SquareFSig. 1 Regression5804.05022902.02515.764.000b Residual3865.90821184.091 Total9669.95823 a. Dependent Variable: OVERRUN b. Predictors: (Constant), WT, HOUSE Coefficientsa ModelUnstandardized Coefficients Standardized Coefficients tSig.95.0% Confidence Interval for B BStd. ErrorBetaLower BoundUpper Bound 1 (Constant)467.95810.72643.627.000445.651490.265 HOUSE13.6832.477.7625.524.0008.53218.835 WT5.5835.539.1391.008.325-5.93617.103 a. Dependent Variable: OVERRUN