Select the null hypothesis for this statistical analysis. Ⓒμ=0 0 0 0 0 Hm-Pnm = 0 Hm - Hnm = 4.2 X -X =0 m nm
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- 15. The following output was obtained from a multiple regression analysis. Analysis of Variance DF Source S MS Regression Residual Error 5 100 20 20 40 2 Total 25 140 Predictor Coefficient SE Coefficient t Constant 3.00 1.50 2.00 X1 4.00 3.00 1.33 X2 3.00 0.20 15.00 X3 0.20 0.05 4.00 X4 -2.50 1.00 -2.50 Xs 3.00 4.00 0.75 a. What is the sample size? b. Compute the value of R2. c. Compute the multiple standard error of estimate. d. Conduct a global test of hypothesis to determine whether any of the regression coefficients are significant. Use the .05 significance level. e. Test the regression coefficients individually. Would you consider omitting any vari- able(s)? If so, which one(s)? Use the .05 significance level.What happens when you overfit a regression model? Question 25 options: The model captures too much of the random variation in the sample data and fails to predict accurately for the population. The p-value for the F-test of overall significance is greater than 0.05. So many variables are added to the model that the Adjusted {"version":"1.1","math":"<math xmlns="http://www.w3.org/1998/Math/MathML"><msup><mi>R</mi><mn>2</mn></msup></math>"} value exceeds 1 None of the relationships in the model are found to be statistically significant.17. Additional information was obtained from EXCEL. Conduct a test of hypothesis to determine if any of the regression coefficients do not equal 0 Use the 005 significance level. The regression equation is: Salary = 19.2 + 3.10 Years + 0.269 Perform - 0.704 Absent Intercept Years Perform Absent Coefficients Standard Error 19.186 3.096 0.269 -0.704 12.146 0.706 0.120 0.586 -1.202 t Stat 1.580 4.385 P-value 0.143 0.001 0.046 0.255 2.252 а. Но: H: Họ: H1: Họ: H1: b. The decision rules are to reject Ho if What is your decision? Interpret. c. 305 Multiple Regression and Correlation Analysis Chapter 14
- In a multiple regression analysis, k=5 and n=31. The MSE value is 11.83 and the SS total is 407.99. At the 0.05 significance level, can we conclude that any of the regression coefficients are not equal to 0? (Round answers to 2 decimal places) Ho: B1=B2=B3=B4=B5=0 H1: Not all B's equal 0 df1 = ? df2 = ? ; so Ho is rejected if F > ? Fill table below Source SS DF MS F Regression ? ? ? ? Error ? ? ? Total 407.99 30After you estimate a simple linear regression you obtain the following sample regression function: Y₁ = 8.8 +0.7091 Xį With an r² of 0.784. The observed dependent variable used for the regression is: Y 9 675 40 40 40 6 5 5 5 5 1 1 Compute the sample variance of X? 8.45 10.79 9.17 7.19 Cannot be computed with the provided information.Please provide evidence of the following. Please provide all derivations used.
- You may need to use the appropriate technology to answer this question. Following is a portion of the computer output for a regression analysis relating y = maintenance expense (dollars per month) to x = usage (hours per week) of a particular brand of computer termina Analysis of Variance SOURCE Regression Error Total Predictor Constant X DF Adj SS 1 1575.76 8 349.14 9 1924.90 Regression Equation Y = 6.1092 +0.8951 X O Ho: B₁ * 0 H₂: B₁ = 0 Coef SE Coef 0.9361 0.1490 (a) Write the estimated regression equation. ý =| 6.1092+ 0.8951r O Ho: B₁ 20 H₂: B₁ <0 |0 Ho: Boo Hà Bo=0 |0 Ho: Bo=0 = 0 Ha: Bo #0 6.1092 0.8951 Ho: B₁ = 0 H₂: B₁ * 0 (b) Use a t test to determine whether monthly maintenance expense (dollars per month) is related to usage (hours per week) at the 0.05 level of significance. State the null and alternative hypotheses. Adj MS 1575.76 43.64 Find the value of the test statistic. (Round your answer to two decimal places.) 36.11 X4. A simple linear regression is fit to a dataset. Unfortunately, the corresponding ANOVA table is not complete because some quantities in the table are missing due to unknown digital errors. Calculate the missing values, denoted by "?", in the ANOVA table based on the other available values. Is this regression model significant (α = 0.05)? Source Regression Residual Total df ? ? 21 SS ? ? ? MS = SS/df ? 1.13 F-Ratio 430.65 p-value ?Our environment is very sensitive to the amount of ozone in the upper atmosphere. The level of ozone normally found is 4.6 parts/million (ppm). A researcher believes that the current ozone level is at an excess level. The mean of 14 samples is 4.9 ppm with a variance of 1.2 Does the data support the claim at the 0.01 level? Assume the population distribution is approximately normal. Step 2 of 5 : Find the value of the test statistic. Round your answer to three decimal places.
- Compute the peast -squares regression line for predecting y from x given the following summary statistics: X= 8.1 s=1.2 y=100 sy= 15 r= 0.70An engineer measures the peak current (in microamps) when a solution containing an amount of nickel (in parts per 10°) is added to a buffer. The experiment was repeated for eleven different values of nickel solutions. A scatterplot showing the data is given below: Peak Current in Eleven Buffers with Added Nickel Solutio 60 120 140 100 Amount of nickel (pp million) Suppose a regression line was added to the plot above. If an additional measurement had been taken with the nickel of 62 parts per 10 and a peak current of 0.38 microamps, adding this observation would: O A. increase the intercept, decrease the slope. OB. increase the intercept, increase the slope. C. decrease the intercept, increase the slope. OD. decrease the intercept, decrease the slope. E. not affect the regression line.You run a regression analysis on a bivariate set of data (n=15). You obtain the regression equationy=2.339x+22.015 with a correlation coefficient of r=0.886 (which is significant at α=0.01). You want to predict what value (on average) for the explanatory variable will give you a value of 150 on the response variable.What is the predicted explanatory value?x = (Report answer accurate to one decimal place.)