The following results are from an independent-measures, two-factor study with n = 10 participants in each treatment condition.Factor A: Factor B:A₁A₂B₁n = 10M = 4T = 40SS = 50n = 10M = 5T = 50SS = 60ΣΧ2 = 640Factor B:B₂n = 10M = 1T = 10SS = 30n = 10M = 2T = 20SS = 40 Use a two-factor ANOVA with a = .05 to evaluate the main effects and interaction.SourceBetween treatmentsABA x BWithin treatmentsTotalSSdfMSF

The results for two-way ANOVA is given. Factor A Factor B B1 B2 n=10 n=10 A1 M=4…

Answered: Use a two-factor ANOVA with a = .05 to…

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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The Cadet is a popular model of sport utility vehicle, known for its relatively high resale value. The bivariate data given below were taken from a sample of sixteen Cadets, each bought new two years ago, and each sold used within the past month. For each Cadet in the sample, we have listed both the mileage x (in thousands of miles) that the Cadet had on its odometer at the time it was sold used and the price y (in thousands of dollars) at which the Cadet was sold used. The least-squares regression line for these data has equation Ŷ=42.80-0.53x. This line is shown in the scatter plot below. (The 2nd picture contains the rest of the data as it would not fit in the first pic and it includes the question as well.)
Interpret this statistic. Why was gamma applicable here? Please show your work.
Let's study the relationship between brand, camera resolution, and internal storage capacity on the price of smartphones. Use α = .05 to perform a regression analysis of the Smartphones01CS dataset, and then answer the following questions. When you copy and paste output from MegaStat to answer a question, remember to choose to "Keep Formatting" to paste the text. a. Did you find any evidence of multicollinearity and variance inflation among the predictors. Explain your answer using a VIF analysis. b. Copy and paste the normal probability plot for your analysis. Is there any evidence that the errors are not normally distributed? Explain. c. Copy and paste the Residuals vs. Predicted Y-values. Does the pattern support the null hypothesis of constant variance for the errors? Explain. d. Study the residuals analysis. Which observations, if any, have unusual residuals? e. Study the residuals analysis. Calculate the leverage statistic. Which observations, if any, are high leverage…
2. Recovering from surgery: A new postsurgical treatment was compared with a stan- dard treatment. Seven subjects received the new treatment, while seven others (the controls) received the standard treatment. The recovery times, in days, are given below. Can you conclude that the mean recovery time for those receiving the new treatment is less than the mean for those receiving the standard treatment? Use the a = 0.05 level. Treatment: Control: H₁: 12 18 (a) List the null and alternate hypetheses. Ho : (c) List the P-value. 13 23 (d) Make the comparison. 15 24 19 30 20 32 (b) List the distribution, the test statistic, the calculator program, and all input and output variables. 21 35 We reject Ho / We do not reject Ho (circle one) (e) State the final conclusion in the wording of the problem. 24 39
Answer the Question 1. In multiple regression: a. the ANOVA table tells us whether the three main regression coefficients (R, R-square, adjusted R-square) are significantly different from 1. .50 2. 1.00 3. Zero 4. 1.50 b. the adjusted R-square can be interpreted as 1. Th percentage of variance accounted for in the dependent variable by the set of independent variables 2. The percentage of variance accounted for in the dependent variable by the set of independent variables minus an estimate penalty 3. The percentage of variance accounted for in the dependent variable by a single independent variable 4. The strength of a relationship between the dependent variable and the set of independent variables

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Use a two-factor ANOVA with a = .05 to evaluate the main effects and interaction. Source Between treatments A B Ax B Within treatments Total SS df MS F