we use the model with age, short-term memory span and IQ as independent variables
y^ = -0.106 + 0.466 age + 0.269 memory span + 0.025 IQ
se = 0.30435
df = 16
point estimate =
y^ = -0.106 + 0.466 age + 0.269 memory span + 0.025 IQ
= -0.106 + 0.466 *6 + 0.269 *4.2 + 0.025*91
= 6.0948
t = t.inv.2t(0.05,16) = 2.1199
95% approx confidence interval
= y^ +- t * 0.4*se
=6.0948 +- 2.12 * 0.4*0.30435
= ( 5.8367 , 6.35288 )
95% approximate prediction interval
= y^ +- t * 1.1*se
= 6.0948 +- 2.12 * 1.1*0.30435
= ( 5.38505 , 6.80454)
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% p...
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...
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...
Regression Variables Entered/Removeda Model Variables Entered Variables Removed Method 1 Warranty_Yearsb . Enter a. Dependent Variable: Number_of_people_mentioned b. All requested variables entered. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .503a .253 .251 .95930 a. Predictors: (Constant), Warranty_Years ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 80.590 1 80.590 87.574 .000b Residual 237.425 258 .920 Total 318.015 259 a. Dependent Variable: Number_of_people_mentioned b. Predictors: (Constant), Warranty_Years Coefficientsa Model Unstandardized...
please help with step by step calculations Variables Entered/Removed Variables Removed Model Variables Entered) Method contraception use (%) Enter a. All requested variables entered. b. Dependent Variable: fertility rate per woman Model Summary Std. Error of the Estimate Model R .71764 Adjusted R2 Square .469 220 168 a. Predictors: (Constant), contraception use %) ANOVA Model Sum of Squares Idf Regression 2.177 Residual 7.725 Total 9.902 Mean Square 2.177 .515 a. Predictors: (Constant), contraception use (%) b. Dependent Variable: fertility rate...
QUESTION 6 Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .641a .410 .406 4.507 a. Predictors: (Constant), age 3 groups, Total Mastery, Total Optimism Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 50.016 1.409 35.508 .000 Total Mastery -.786 .067 -.526 -11.719 .000 Total Optimism -.217 .060 -.164 -3.623 .000 age 3 groups -.712 .275 -.098 -2.588 .010 a. Dependent Variable: Total perceived stress What proportion of...
Overview of the Study: The data are based on a Comprehensive School Reform (CSR) Initiative that focused on the improvement of reading and writing for students in the primary grade. The school received a grant from the state which was used to strengthen classroom teachers’ instructional skills. The regression outputs present information for students in the school. Description of the variables: Please use the following description/coding to help you in your analyses. Gender: female; 1 male=0 Coding – Gender female...
Linear regression analysis of the data revealed the following: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .695a .483 .478 13.02473 a. Predictors: (Constant), exercise, gender, subject's age, depressed state of mind ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 65230.870 4 16307.718 96.129 .000b Residual 69893.149 412 169.644 Total 135124.019 416 a. Dependent Variable: Life Purpose and Satisfaction b. Predictors: (Constant), exercise, gender, subject's age, depressed state of...
Dummy Variable Regression: Choose any metric variable as the dependent variable (you can use the same one that you used in Part A) and choose gender as an independent variable. Also choose one more metric variable as an additional independent variable. Once again, however, you must sort through the metric independent variables until you find one that, along with gender, produces a significant F-calc. Use alpha = .05 here as well. You only need to report the model that produced...
43 college students 44% male, 56% female Students reported on the number of hours spent studying per week (0-40 hours), their life satisfaction (scale from 0-100), degree of stress they experienced over the last month (scale 0-5), and completed an IQ test (40-160). Students also reported their gender (1=male, 2=female) and cumulative GPA. For the statistical analysis performed, you need to provide responses to two questions: What type of statistical analysis was used to examine what kind of research question?...