Suppose that n = 50, i.i.d observations for (Y₁, X₁) yield the following regressions results: Ỹ= 49.2 + 73.9 X, SER (23.5) (16.4) = 13.4, R²=0.78 Another researcher is interested in the same regression, but makes an error when entering the data into a regression program: The research enters each observation twice, ending up with 100 observations (with observation 1 entered twice, observation 2 entered twice and so forth). Using these 100 observations, what results will be produced by the regression program? Complete the spaces in the equation below. Report the intercept and slope to one decimal place, but report the R2 to two decimal places. Ŷ= + *X, R² =

A regression was run to determine if there is a relationship between hours of study per week (xx) and the final exam scores (yy).The results of the regression were: y=ax+b a=6.22 b=21.6 r2=0.477481 r=0.691 Use this to predict the final exam score of a student who studies 8 hours per week, and please round your answer to a whole number.

Using 23 observations on each variable, a computer program generated the following multiple regression model. y = 65.5 +9.56x1+3.27x2-4.23x3– 9.23x4 If the standard errors of the coefficients of the independent variables are, respectively, 3.44, 2.30, 2.76, and 4.99, can you conclude that the independent variable x, is needed in the regression model? Let B1, B2, Ba denote the coefficients of the 4 variables in this model, and use a two-sided hypothesis test and significance level of 0.10 to determine your 4 .../ answer. (a) State the null hypothesis H, and the alternative hypothesis H,. H, : 0 H, : 0 (b) Determine the type of test statistic to use. Degrees of freedom: D=0 OSO (c) Find the value of the test statistic. (Round to two or more decimal places.) O#0 OO (d) Find the two critical values at the 0.10 level of significance. (Round to two or more decimal places.) | and || (e) Can you conclude that the independent variable is needed in the regression model? Yes No 미

A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=ax+b a=-1.307 b=32.392 r²=0.675684 r=-0.822 Use this to predict the number of situps a person who watches 12.5 hours of TV can do (to one decimal place)