Hi this question may have been posted before do not know just looking for assistance and having difficulties, the question comes from my online discussion forum post (SCMG 305 Global demand management) Investigate the cause-and-effect relationships utilizing regression analysis, find one authoritative resource in the form of a U-tube video or Website that explains the use of regression analysis as a prediction model for forecasting. Try not to duplicate a resource already posted by another student. Insert a hyperlink for that resource so others may access it quickly. Finally, provide a quick short paragraph/summary explaining what you learned from the resource you provided strengthening your understanding of the use of regression analysis for forecasting.

I need Explaination based on the answer key answer is D but how. It is econometrics

Find the degrees of freedom in a regression model that has 40 observations, 6 independent variables and one intercept.  a. 33  b. 47  c. 7  d. 39

In a regression model, if the variance of the dependent variable, y, conditional on an explanatory variable, x, or Var(y/x), is not constant, a. the t statistics are invalid and the confidence intervals are valid for small sample sizes. b. the t statistics are valid and the confidence intervals are invalid for small sample sizes. c. the t statistics and the confidence intervals are valid no matter how large the sample size. d. the t statistics and the confidence intervals are both invalid no matter how large the sample size. O a. a O b. b О с. C ○ d. d

You are interested in how the number of hours a high school student has to work in an outside job has on their GPA. In your regression you want to control for high school standing and so you run the following regression: GPA = 3.4 0.03 * HrsWrk - 0.7 * Frosh - 0.3 * Soph +0.1 * Junior (1.1) (0.013) (0.23) (0.14) (0.08) where HrsWrk is the number of hours the student works per week, and Frosh, Soph, and Junior are dummy variables for the student's class standing. a) If you include a dummy variable for seniors, that would cause a Hint: type one word in each blank. For the rest of questions, type a number in one decimal place. b) The expected GPA of a Sophomore who works 10 hours per week is c) The expected GPA of a Senior who works 10 hours per week is d) If Dom and Sarah work the same number of hours per week, but Dom is a Junior and Sarah is a Freshman. Dom is expected to have a higher GPA than Sarah. e) Suppose you rewrite the regression as: problem. GPA = ₁HrsWrk + ß2Frosh + B2Soph +…

Please help me with both the question. Answer there are incorrect

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An analyst working for your firm provided an estimated log-linear demand function based on the natural logarithm of the quantity sold, price, and the average income of consumers. Results are summarized in the following table: SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept LN Price LN Income df 0.968 0.937 0.933 0.003 30 SS MS F 2 0.003637484 0.001818742 202.48598 0.000242516 8.98206E-06 27 29 0.00388 Coefficients Standard Error 0.57 0.00 0.13 0.51 -0.08 0.15 t Stat 0.90 -19.50 1.13 P-value 0.37 0.00 0.27 Significance F 5.55598E-17 Lower 95% -0.65 -0.09 -0.12 How would a 4 percent increase in income impact the demand for your product? Demand would increase by 60 percent. Demand would increase by 0.6 percent. Demand would decrease by 60 percent. Demand would decrease by 0.6 percent. Upper 95% 1.68 -0.07 0.41

Please provide me with the correct answer, along with the calculations, and do not use any AI tools

Define coefficients of the Linear Regression Model?