Please see contents in the image attached for questions below: Thank you!a) What is dependent and independent variables? b) Fully write out the regression equation c) What is the sample size used in this investigation? d) Fill in the blanks identified by ‘*’ and ‘**’.e)Is β significant, at the 5% level of significance? The following regression is fitted using variables identified that could be related to studentrequestedloan amount, LOAN-AMT($) for returning students of a certain University.LOAN AMT = a + ß ACCEPT + y PREV + OUTSWhere ACCEPT the percentage of applicants that was accepted by the university, PREV=previous loan amount and OUTS outstanding loan amountThe data was processed using MNITAB and the following is an extract of the output obtained:PredictorConstantACCEPTPREVOUTSS = 2685Coef-26780116.00-4.2170.85R-Sq = 69.6%Analysis of VarianceSourceDFRegression3Residual Error 49TotalEXHIBIT 2St Dev611537.1714.1215.778081393713531930511161332421H* MT3.14-0.304.49MS0.0000.00326937979072080210.000R-Sq (adj) = 67.7%F37.37P0.00

The provided regression model isThe MNITAB summary output of the regression analysis is…

Please see contents in the image attached for questions below: Thank you! a) What is dependent and independent variables? b) Fully write out the regression equation c) What is the sample size used in this investigation? d) Fill in the blanks identified by ‘*’ and ‘**’. e)Is β significant, at the 5% level of significance?

Please see contents in the image attached for questions below: Thank you! a) What is dependent and independent variables? b) Fully write out the regression equation c) What is the sample size used in this investigation? d) Fill in the blanks identified by ‘*’ and ‘**’. e)Is β significant, at the 5% level of significance?

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Fifty matched pairs of magnitude/depth measurements randomly selected from 10,594 earthquakes recorded in one year from a location in southern California. Find the best predicted depth of an earthquake with a magnitude of 2.2. Use a significance level of 0.05. : Click the icon to view the earthquake measurements. Click here to view the critical values of the Pearson correlation coefficient, r. The regression equation is y = ? + ?x. 回-ト i Critical Values of the Pearson Correlation Coefficient r (Round to one decimal place as needed.) Critical Values of the Pearson Correlation Coefficient r α= 0.05 0.950 0.878 0.811 0.754 0.707 0.666 0.632 0.602 0.576 a = 0.01 0.990 0.959 0.917 0.875 0.834 0.798 0.765 0.735 0.708 NOTE: To test Ho: p=0 Jagainst H,: p#0, reject H, if the absolute value of r is greater than the critical value in the table. 5 10 11 12
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APPLIED STATISTICS (MULTIPLE REGRESSION) UPVOTE WILL BE GIVEN. PLEASE ANSWER THE PROBLEM BY USING SPSS OR EXCEL ONLY (NOT HANDWRITTEN). NO LONG EXPLANATION NEEDED. TAKE A SCREENSHOTS AND ANSWER THE QUESTION IN SENTENCES. a. Identify the dependent/s and independent variable then estimate the multiple regression equation. b. What is the r-squared value and what is its implication? c. What variable/s are considered significant? What is the p-value?
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Data were collected to explain the number of wins an NFL team has based on the average points per game that they score (PPG) Based on the regression results, answer the following questions a) What is the estimated regression equation? b) What percentage of the wins is explained by PPG? c) What is the standard error of the error term in the regression equation?
4. A study was conducted to investigate the relationship between the size of a house (in square feet) and the selling price of a house (in dollars). The response variable is price in dollars, and we want to study if the covariate of the square footage helps explain the response. A random sample of 522 houses was used, and the linear regression output from R is below. price sqft, data = house) 1m (formula = Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -81432.946 11551.846 -7.049 5.74e-12 *** sqft 158.950 4.875 32.605 <2e-16 *** Residual standard error: 79120 on 520 degrees of freedom Multiple R-squared: 0.6715, Adjusted R-squared: 0.6709 a. Write out the estimated linear equation. What is the estimated expected selling price of a house that is 2000 square feet? b. Does the intercept have a useful interpretation in this study? Why or why not. c. Interpret the slope estimate in context of the model.
o pis pe Suppose a doctor measures the height, x, and head circumference, y. of 11 children and obtains the data below. The correlation coefficient is 0.899 and the least squares regression line is y = 0.185x+ 12.276. Complete parts (a) and (b) below. Height, x Head Circumference, y 17.4 17.1 17.2 16.9 17.4 17.1 17.1 17.3 17.3 17.3 17.4 27.75 25.75 26.75 25.75 28 26.5 25.75 26.75 27 27.25 27.25 (a) Compute the coefficient of determination, R?. R2 = % (Round to one decimal place as needed) (b) Interpret the coefficient of determination k Approximately % of the variation in (Round to one decimal place as needed.) is explained by the least-squares regression model. Enter your answer in each of the answer boxes. Save for Later DUE RTTT:DY PIVT Score: o:5SYO Tor Tv attempts 3:35 PA- a 2/22/202 Type here to search insert prt sc 4+ backsp %23 6. 8.
7) Below is a multiple regression in which the dependent variable is market value of houses and the independent variables are the age of the house and square footage of the house. The regression was estimated for 42 houses. SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total df 2 39 41 0.745495 0.555762 0.532981 7211.848 42 SS 2537650171 2028419591 4566069762 Coefficients Standard Error MS 1.27E+09 52010759 F 24.39544 Significance F 1.3443E-07 Upper 95% t Stat P-value Lower 95% Intercept 47331.38 13884.34664 3.408974 0.001528 19247.6673 House Age -825.161 607.3128421 -1.35871 0.182046 -2053.5662 Square Feet 40.91107 6.696523994 6.109299 3.65E-07 27.3660835 7A. What is the estimated regression equation for determining the market value of houses? 7B. Discuss tests of significance of the regression 7C. What percentage of the variation in the dependent variable, Market Value, is explained by the regression…
The ANOVA summary table to the right is for a multiple regression model with six independent variables. Complete parts (a) through (e). Draw a conclusion. Choose the correct answer below. (3) Degrees of Source Freedom Regression Error Total 6 26 32 Sum of Squares 240 190 430 A. There is insufficient evidence of a significant linear relationship with at least one of the independent variables because the test statistic is less than the critical value. O B. There is sufficient evidence of a significant linear relationship with at least one of the independent variables because the p-value is less than the level of significance. C. There is sufficient evidence of a significant linear relationship with at least one of the independent variables because the test statistic is greater than the level of significance. D. There is insufficient evidence of a significant linear relationship with at least one of the independent variables because the test statistic is greater than the critical value.
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Consider the following ANOVA table for a multiple regression model. Source df SS MS F Regression 3 225 75 5 Residual 20 300 15 Total 23 525 a) what is the size sample? How many independent variables are in this model? b) Calculate the multiple coefficient of determination? d) Test the significance of the overall regression model using a= 0.10. -what is the p-value? e) What is the conclusion for this test? f) Calculate the adjusted multiple coefficient of determination. Can I please have help. A previous expert gave me wrong answers.
A car dealership would like to develop a regression model that would predict the number of cars sold per month by a dealership employee based on theemployee's number of years of sales experience. The accompanying regression output was developed based on a random sample of employees. a. Predict the sales next month for an employee with 2.5 years of experience. The predicted sales is 8.6 cars. b. Compute the coefficient of determination and interpret its meaning. The coefficient of determination is 0.234 Therefore, about _________% of the variation in monthly sales is explained by the years of sales experience. (Type an integer or decimal rounded to one decimal place as needed.)