Solution :
The null and alternative hypotheses are as follows :
To test the hypothesis t-test will be used. The test statisical is given as follows :
From the given output we have,
The value of the test statistic is 3.65554.
Degrees of freedom = (n - 2) = (5 - 2) = 3
The two-tailed p-value for the test statistic is given as follows :
p-value = 2.P(T > t)
p-value = 2.P(T > 3.65554)
Using R software we get,
p-value = 0.0354
The p-value is 0.0354.
Hence, option (b) is correct.
Please rate the answer. Thank you.
10. The following is the simple linear regression analysis output: E(Y) = Bo + B1 (ADV_X)...
Using output given below, (a) Set-up simultaneous 95% confidence intervals for Bo and B1. (b) Set-up a 95% confidence interval for u{5.1}= expected vo at a speed of 5.1. (c) What is the covariance between bo and by? What does it mean? OUTPUT 1, Speed Example The SAS System Obs speed vo 1 4.0 3.5 15.7 18.4 4.5 22.0 5.0 34.8 5.5 45.3 6.0 51.1 The SAS System The REG Procedure Model: MODEL 1 Dependent Variable: vo Analysis of Variance...
3 (20%) Brian once fits a simple regression model, but the printout is somehow incomplete due to the shortage of toner cartridge. Please help him with finishing up the missing entries MS value Pr>F Source Model Error Corrected Total 133152.95429 DE SS 1 3126.13385 3126.13385 1398.69 .0001 12 2.23504 Root MSE 1.49500 R-Square 0.9915 Dependent Mean 37.15714 Adj RSq(b) Coeff Var 4.02346 Intercept1(c)1.57501 59.77.0001 90.70242 97.56571 1.19239 Temp1 -1.26615 (d) 3,0001(e) 0.03386 -
4. Let’s compare the results you calculated for Q3b with results from a multiple linear regression. 4a. Would additionally controlling for ‘depth’ and ‘latitude’ be helpful? In other words, is a model that includes ‘depth’, ‘latitude’ and ‘longitude’ superior in model fit to a model that includes only ‘longitude’? Output for a multiple linear regression which includes longitude, depth, and latitude is provided below. (2 points) 4b. Interpret the parameter estimate for ‘longitude’ from the multiple linear regression output. (1...
A simple linear regression (linear regression with only one predictor) analysis was carried out using a sample of 23 observations From the sample data, the following information was obtained: SST = [(y - 3)² = 220.12, SSE= L = [(yi - ġ) = 83.18, Answer the following: EEEEEEEE Complete the Analysis of VAriance (ANOVA) table below. df SS MS F Source Regression (Model) Residual Error Total Regression standard error (root MSE) = 8 = The % of variation in the...
(10 points) The following regression output is available. Notice that some of the values are missing. Predictor Coef SE Coef T P Constant 5.932 2.558 2.320 0.068 x 0.511 6.083 0.001 Analysis of Variance Source DF SS MS F P Regression 648.72 648.72 57.20 0.001 Residual Error 56.70 Total 16 705.43 Based on the information given, what is the value of sum of squares of the X’s (SSxx)? 7626.92 23.142 535.591 None of the above 1. (10 points) Consider the following partially completed computer printout for a regression analysis Based on the information provided, which of the following statements is true at a...
ANOVA A study is designed to examine whether there is a difference in mean daily calcium intake among three groups of adults with normal bone density (Norm), adults with osteopenia (OstPNia) (a low bone density which may lead to osteoporosis) and adults with osteoporosis (OstPSis). A total of twenty-one adults at age 60 was recruited in the study (7 adults in each group). Each participant's daily calcium intake was measured based on reported food intake and supplements in milligrams. We...
Dependent Variable: Y $1000 fire damage Analysis of Variance Sum of Mean Source DF Squares Square F Value Pro Model 1 841.76636 841.76636 156.886 0.0001 Error 13 69.75098 5.36546 Total(Adjusted) 14 911.51733 Root MSE 2.31635 R-square 0.9235 Dep Mean 26.41333 Adj R-sq 0.9176 C.V. 8.76961 Parameter Estimates Parameter Standard T for H0: Variable Estimate Error Parameter=0 Prob > |T| INTERCEPT 10.277929 1.42027781 7.237 0.0001 X 4.919331 0.39274775 12.525 0.0001 Dep Actual Predicted 95% LCL 95% UCL 95% LCL 95% Obs ...
Which model is more appropriate for these data: the model in SAS Output 1 or the model in SAS Output 2? Which test statistic and p-value should you use to make this decision? Output 1 because the interaction is not significant (F = 0.92, p-value = 0.4594). Output 1 because the interaction is not significant (F = 6.25, p-value = 0.0003). Output 1 because the interaction is significant (F = 6.25, p-value = 0.0003). Output 2 because the interaction is...
iated prob- SAS output of a regression analysis of th gasoline mileage data using the model y o+ e United Oil Company premiunm SAS DEP VARIABLB: ILEAGE ANALYSIS OF VARIANCE SUV OF QUARRS BAN SQUARE F VALUE PROB) SOURCE DF MODEL ERROR C TOTAL 21 127.47273 6 120. 56404 20.09400691 43.628 0.0001 15 6.90868583 0.46057906 ROOT MS® DEP MEAN C. v 0. 6786597 32. 11818 2. 113008 R-SQUARE ADJ R-SQ .9458 0.9241 PARAMETER ESTINATES PARAMETER ESTIMATE STANDARD ERROR T POR...
please complete from A to E An observational study of teams fishing for the red spiny lobster in a certain city was conducted and the results are attached below. Two variables measured for each of 8 teams were y=total catch of lobsters (in kilograms) during the season and x = average percentage of traps allocated per day to exploring areas of unknown catch (called search frequency). These data are listed in the table. Complete parts a through e below. Click...