Question

Below is the R output of a study of ACT scores for the first year of...

Below is the R output of a study of ACT scores for the first year of college students. This helps to see if the test scores can predict a GPA. Simply put, this ACT helps to be an explanatory var and GPA would be a response var.

Call:

Im(formula = GPA ~ ACT, data = gpadata)

Residuals:

Min 1Q Median 3Q Max

-2.74004 -0.33827 0.04062 0.44064 1.22737

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) 2.11405 .32089 6.588 1.3e-09 ***

ACT .03883 .01277 3.040 .00292 **

_ _ _

Signif codes: 0 '***' .001 '**' .01 '*' .05 '.' .1 '' 1

Residual standard error: .6231 on 118 DFreedom

Multiple R Squared: .07262, Adjusted R Squared: .06476

F-Stat: 9.24 on 1 and 118 DF, p-val: .002917

ANOVA data is:

Call: aov(formula = gpa.Im)

Terms:

ACT Residuals

Sum of Squares 3.58785 45.81761

Deg Freedom 1 118

Residual stand. error : .623125

- What's the value of intercept.

-Give confidence interval for 95% for true correlation between GPA and ACT

-If a student received a 34 on the ACT, then create a 95% prediction interval of the student's first year GPA. The sample mean ACT is 24.72

-Find the value for the MSM:

-Correleation between GPA and ACT scores?

Homework Answers

Answer #1

I can't understand what is MSM?

Know the answer?
Your Answer:

Post as a guest

Your Name:

What's your source?

Earn Coins

Coins can be redeemed for fabulous gifts.

Not the answer you're looking for?
Ask your own homework help question
Similar Questions
Can you give me a simple interpretation of this output? Call: lm(formula = NOCRF ~ Mktrf...
Can you give me a simple interpretation of this output? Call: lm(formula = NOCRF ~ Mktrf + HML + SMB + SMB2) Residuals:      Min       1Q   Median       3Q      Max -10.1560 -0.6880 -0.0254 0.6660 21.9700 Coefficients:             Estimate Std. Error t value Pr(>|t|)    (Intercept) -0.01163    0.02800 -0.415    0.678    Mktrf        1.25614    0.02389 53.540 <2e-16 *** HML          2.01719    0.04238 47.602   <2e-16 *** SMB         -0.05150    0.04769 -1.080    0.280    SMB2         0.03180    0.03545   0.897    0.372 --- Signif. codes: 0 ‘***’ 0.001...
Using the following data taking out of R (summary): Call: lm(formula = dys_detect ~ fin_loss, data...
Using the following data taking out of R (summary): Call: lm(formula = dys_detect ~ fin_loss, data = Lab5, na.action = na.exclude) Residuals: Min 1Q Median 3Q Max -582.66 -274.75 13.53 273.92 589.06 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 385.362 77.360 4.981 8.72e-07 *** fin_loss 3.248 1.523 2.133 0.0334 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 316.6 on 498 degrees of freedom Multiple R-squared: 0.009052,   Adjusted R-squared:...
Consider the following regression run in R, which uses engine size in liters, horsepower, weight, and...
Consider the following regression run in R, which uses engine size in liters, horsepower, weight, and domestic vs foreign manufacturer to predict mileage: ------------------------------------------------------------------------------------------------------ > summary(lm(highwaympg~displacement+hp+weight+domestic)) Call: lm(formula = highwaympg ~ displacement + hp + weight + domestic) Residuals:     Min      1Q  Median      3Q     Max -6.9530 -1.6997 -0.1708 1.6452 11.4028 Coefficients:               Estimate Std. Error t value Pr(>|t|)    (Intercept) 53.849794   2.090657 25.757 < 2e-16 *** displacement 1.460873   0.748837   1.951   0.0543 . hp           -0.009802   0.011356 -0.863   0.3904    weight       -0.008700   0.001094 -7.951 6.23e-12 *** domestic     -0.939918   0.762175 -1.233   0.2208    ---...
8.) Now, do a simple linear regression model for LifeExpect2017 vs. AverageDailyPM2.5. For credit, provide the...
8.) Now, do a simple linear regression model for LifeExpect2017 vs. AverageDailyPM2.5. For credit, provide the summary output for this simple linear regression model. > Model2 <- lm(LifeExpect2017~ AverageDailyPM2.5) > summary(Model2) Call: lm(formula = LifeExpect2017 ~ AverageDailyPM2.5) Residuals: Min 1Q Median 3Q Max -17.1094 -1.7516 0.0592 1.7208 18.4604 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 81.6278 0.2479 329.23 <2e-16 *** AverageDailyPM2.5 -0.4615 0.0267 -17.29 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘...
> muncy = lm(hit_distance~launch_speed, data=muncy) > summary(muncy) Call: lm(formula = hit_distance ~ launch_speed, data = muncy)...
> muncy = lm(hit_distance~launch_speed, data=muncy) > summary(muncy) Call: lm(formula = hit_distance ~ launch_speed, data = muncy) Residuals:     Min      1Q Median      3Q     Max -258.24 -105.23   23.29 116.06 174.73 Coefficients:               Estimate Std. Error t value Pr(>|t|)    (Intercept) -240.8429    36.6769 -6.567 1.46e-10 *** launch_speed    4.8800     0.4022 12.134 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 122.4 on 438 degrees of freedom Multiple R-squared: 0.2516, Adjusted R-squared: 0.2499 F-statistic:...
Marketing date on sales is presented for youtube. data are the advertising budget in thousands of...
Marketing date on sales is presented for youtube. data are the advertising budget in thousands of dollars along with the sales. The experiment has been repeated 200 times with different budgets and the observed sales have been recorded. The simple linear regression model was fitted: ## ## Call: ## lm(formula = sales ~ youtube, data = marketing) ## ## Residuals: ## Min 1Q Median 3Q Max ## -10.06 -2.35 -0.23 2.48 8.65 ## ## Coefficients: ## Estimate Std. Error t...
Residuals:     Min      1Q Median      3Q     Max -6249.5 -382.9 -139.3    25.6 31164.7 Coefficients:         &nbs
Residuals:     Min      1Q Median      3Q     Max -6249.5 -382.9 -139.3    25.6 31164.7 Coefficients:               Estimate Std. Error t value Pr(>|t|)    (Intercept) 1.311e+02 2.219e+02   0.591   0.5550    debt         1.283e-01 3.288e-01   0.390   0.6966    sales        2.942e-01 1.366e-01   2.154   0.0321 * income       1.546e+01 2.697e+00   5.730 2.42e-08 *** assets      -2.390e-05 4.839e-03 -0.005   0.9961    seo          2.973e+02 2.627e+02   1.132   0.2587    --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2019 on 303 degrees of freedom Multiple R-squared: 0.258,   Adjusted...
IV. Your friend mentions that pre-test measures was taken and wants to take that information into...
IV. Your friend mentions that pre-test measures was taken and wants to take that information into account. You suggest using the pre-test value as a covariate to see what effect does it have on the response. First you fit a simple linear regression of diastolic on pre-diastolic. Call: lm(formula = diastolic ~ prediastolic, data = a) Residuals:     Min      1Q Median      3Q     Max -11.268 -4.627   1.101   3.652 10.644 Coefficients:              Estimate Std. Error t value Pr(>|t|)    (Intercept)   30.7307     9.9041   3.103...
4.-Interpret the following regression model Call: lm(formula = log(Sale.Price) ~ Lot.Size + Square.Feet + Num.Baths +...
4.-Interpret the following regression model Call: lm(formula = log(Sale.Price) ~ Lot.Size + Square.Feet + Num.Baths + dis_coast + API.2011 + dis_fwy + dis_down + Pool, data = Training) Residuals: Min 1Q Median 3Q Max -2.17695 -0.23519 -0.00112 0.26471 1.02810 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 9.630e+00 2.017e-01 47.756 < 2e-16 *** Lot.Size -2.107e-06 3.161e-07 -6.666 4.78e-11 *** Square.Feet 2.026e-04 3.021e-05 6.705 3.71e-11 *** Num.Baths 6.406e-02 2.629e-02 2.437 0.015031 * dis_coast -1.827e-05 6.881e-06 -2.655 0.008077 ** API.2011 3.459e-03 2.356e-04...
R Linear Model Summary. Based on the R output below, answer the following: (a) What can...
R Linear Model Summary. Based on the R output below, answer the following: (a) What can infer about β0 and/or β1 ? (b) What is the interpretation of R2 . (Non-Adjusted) ? In particular, what does it say about how “x explains y” (c) Perform the test (α = 0.05): H0 : ρ = 0.5; Ha : ρ > 0.5 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.32632 0.24979 1.306 0.194 x 0.09521 0.01022 9.313 2.93e-15 *** --- Signif....