Solution:
Part A:
The estimated Regression equation is:
SAT = 1112.769 - 23.918 Expend
Part B:
Part C:
The regression model is not a good fit this is because... The value of R2 is not close to 1. The value is 0.205 which means that only 20.5% of the variations in the dependent variables are explained by the independent variables which doesn't signifies a good fit...
The value of significance F in the Anova table is 0.001 which is less than 0.05.. This means that the model is statistically significant which is a good sign..
Part D:
The regression equation is:
SAT = 1112.769 - 23.918 Expend
Expend will be at at the average of the 48 states = 5.946
So, SAT = 1112.769 - 23.918 * 5.946
So, SAT = 970.5
So, by increasing the expenditure by the average of 48 states will generate a SAT score of 970.5.
End of the Solution..
DISPLAY A Descriptives Descriptive Statistics N Minimum Maximum Mean Std. Deviation 1.376 72.673 ...
DESCRIPTIVE STATISTICS: Create a table of descriptive statistics with an appropriate tabular format. See the Output A[2 pt]. STATISTICAL RESULTS: Report and statistically interpret the results with an appropriate tabular format and text. See the Output B. [6 pt] DISCUSSION: Discuss what the results imply. [2 pt] Output A. Descriptive statistics Statistics Exam Performance (%) Exam Anxiety N Valid 103 103 Missing 0 0 Mean 56.57 74.3437 Median 60.00 79.0440 Std. Deviation 25.941 17.18186 Minimum 2 1.00 Maximum 100 100.00...
Investigate the relationship between the respondent’s education (EDUC) and the education received by his or her father and mother (PAEDUC and MAEDUC, respectively). Calculate the correlation coefficient, the coefficient of determination, and the regression equation predicting the respondent’s education with father’s education only. Interpret your results. Determine the multiple correlation coefficient, the multiple coefficient of determination, and the regression equation predicting the respondent’s education with father’s and mother’s education. Interpret your results. Did taking into account the respondent’s mother’s education...
WERE depression /METHOD-ENTER daddrink momdrink DIF. Regression 214 Descriptive Statistics Mean Std. Deviation 4.0514 4.13188 2.5234 3.09428 9673 1.73445 15.2103 6.02870 depression total fsmast momdrink DIF 214 214 214 Pearson Correlation depression total fsmast momdrink -104 .011 1.000 038 momdrink DIF 471 .111 038 1.000 .000 .053 291 DIF Sig. (1-tailed) 064 Correlations depression total fsmast 1.000 227 227 1.000 104 .011 -471 .111 .000 .000 .064 436 .000 .053 214 214 214 214 214 214 214 depression total fsmast...
Models 1-7 are below Part C: Select one model you would use to explain reading ability.,Then use that model to find the 95% confidence interval estimate for the mean reading ability 95% prediction interval for reading ability When age 6, mem span 4.2 and ig 91. Regression [DataSetll C:\Usersn.little5773 Downloads\child data.sav Variables Entered/Removed Variables Entered Variables Removed Method Model Enter age a. Dependent Variable: reading ability b. All requested variables entered. Model Summary Adjusted R Square Std. Error o R...
From the three three Regression tests, come up with three hypotheses. Regression Method Variables Entered/Removeda Variables Model Variables Entered Removed 1 TotElectb a. Dependent Variable: Variety Seeking b. All requested variables entered. Enter Model Summary Adjusted R R Square Square .009 .002 Model R Std. Error of the Estimate .64205 1 .0958 a. Predictors: (Constant), TotElect Coefficients a Standardized Coefficients Model Unstandardized Coefficients B Std. Error 3.667 . 108 Beta t Sig. .000 1 (Constant) 34.075 TotElect .008 .007 .095...
Model Summary Adjusted R Square Std. Error of the Estimate Model R R Square 1 .843a .711 .707 7.812812 a. Predictors: (Constant), Fuel efficiency, Horsepower Coefficientsa Standardized Coefficients Beta Sig 2.354 .020 Unstandardized Coefficients Model B Std. Error 1 (Constant) 28.144 11.954 Horsepower 229 .013 Length - 219 Fuel efficiency -.090 .185 a. Dependent Variable: Price in thousands .906 16.989 ,000 .050 - 205 -4.348 .000 -.027 -.488 .627 Model Summary Adjusted R Square Std. Error of the Estimate Model...
6. Interpreting statistical software output in regression Aa Aa Suppose you work in the admissions department of a small liberal arts college. You wonder if you can predict students' college grade point averages (GPAs) by their SAT scores. You randomly select 50 recent graduates and collect their SAT scores and college GPAs. You use a statistical software package to run a regression predicting college GPA from SAT score. Use the following output to answer the questions that follovw Descriptive Statistics...
A researcher uses two regression models to seek answers to two research questions. These models are: Y1 = Bo1 + B11X1 Y2 = Bo2 + B12X1 + B22X12 Test the null hypotheses for both models. Use the results of your analyses to recommend an appropriate model. In each of the above two cases, state your null and alternative hypotheses, decision criteria, decision and conclusion. The level of significance is 5%. The data for this study are presented in the table...
Model Summary Change Statistics Adjusted Std. Enor of R Square Model R R Square Square the Estimate 657 432 2904205716161 4 32 a. Predictors: (Constant, eigencentrality, Average 15All optimice_threshold_b F Changed 3.0393 Sig F Change .071 12 ANOVA Sig 071 Sum of Model Squares Mean Square Regression 1.612 537 3.099 Residual 2.12) 12 Total 3.735 a Dependent Variable: average_MSEMSE b. Predictors: (Constant. eigencentrality Average 15Aoptimized_threshold_b Coefficients Standardized Coeficients Beta Collinearity Stanisses Tolerance Model Sia 000 Unstandardized Coeficients B Sid Error...
The following questions refer to the output shown below. Researchers used temperature to predict failure time for a superconductive material with the following a. Write the regression equation based on the results shown below. b. Assess the model utility. linear model: yˆ = β0 + β1xtemp Write the regression equation based on the results shown below. Would you recommend the model? Why or why not? Model Summary Adjusted R Model R R Square Square .918a .843 .835 a. Predictors: (Constant),...