[HW9] Hypothesis Testing and Regression Analysis

using the following ANOVA Table for regression analysis to compute the adjusted R^2 value

Conduct a global test on the set of independent variables. Interpret   Regression Statistics               Multiple R 0.87027387               R Square 0.75737661               Adjusted R Square 0.75615535               Standard Error 14.6932431               Observations 600                                 ANOVA                   df SS MS F Significance F       Regression 3 401662.063 133887.354 620.160683 8.708E-183       Residual 596 128671.271 215.891394           Total 599 530333.333                                 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -3.9995369 3.05935528 -1.3073137 0.19161031 -10.007965 2.00889078 -10.007965 2.00889078 Annual Income 0.0002132 3.1402E-05 6.78944156 2.7269E-11 0.00015153 0.00027487 0.00015153 0.00027487 Married 45.7808695 1.20203164 38.0862434 8.444E-162 43.4201368 48.1416023 43.4201368 48.1416023 Male 21.9175699 1.20122625 18.2459964 2.0045E-59…

A multiple regression analysis produced the following tables. Summary Output                     Regression Statistics                     Multiple R   0.978724022                 R Square   0.957900711                         Adjusted R Square   0.952287472                 Standard Error   67.67055418                 Observations   18                                             ANOVA                         df   SS   MS   F   Significance F Regression   2   1562918.941   781459.5   170.6503   4.80907E-11 Residual   15   68689.55855   4579.304         Total   17   1631608.5                                       Coefficients   Standard Error   t Stat   P-value     Intercept   1959.709718   306.4905312   6.39403   1.21E-05     X1   -0.469657287   0.264557168   -1.77526   0.096144     X2   -2.163344882   0.278361425   -7.77171   1.23E-06     The regression equation for…

A multiple regression analysis produced the following tables. Summary Output                     Regression Statistics                     Multiple R   0.978724022                 R Square   0.957900711                         Adjusted R Square   0.952287472                 Standard Error   67.67055418                 Observations   18                                             ANOVA                         df   SS   MS   F   Significance F Regression   2   1562918.941   781459.5   170.6503   4.80907E-11 Residual   15   68689.55855   4579.304         Total   17   1631608.5                                       Coefficients   Standard Error   t Stat   P-value     Intercept   1959.709718   306.4905312   6.39403   1.21E-05     X1   -0.469657287   0.264557168   -1.77526   0.096144     X2   -2.163344882   0.278361425   -7.77171   1.23E-06     Using α = 0.01 to test the…

A multiple regression analysis produced the following tables. Summary Output                     Regression Statistics                     Multiple R   0.978724022                 R Square   0.957900711                         Adjusted R Square   0.952287472                 Standard Error   67.67055418                 Observations   18                                             ANOVA                         df   SS   MS   F   Significance F Regression   2   1562918.941   781459.5   170.6503   4.80907E-11 Residual   15   68689.55855   4579.304         Total   17   1631608.5                                       Coefficients   Standard Error   t Stat   P-value     Intercept   1959.709718   306.4905312   6.39403   1.21E-05     X1   -0.469657287   0.264557168   -1.77526   0.096144     X2   -2.163344882   0.278361425   -7.77171   1.23E-06     For x1= 360 and x2 = 220, the…

A multiple regression analysis produced the following tables. Summary Output                     Regression Statistics                     Multiple R   0.978724022                 R Square   0.957900711                         Adjusted R Square   0.952287472                 Standard Error   67.67055418                 Observations   18                                             ANOVA                         df   SS   MS   F   Significance F Regression   2   1562918.941   781459.5   170.6503   4.80907E-11 Residual   15   68689.55855   4579.304         Total   17   1631608.5                                       Coefficients   Standard Error   t Stat   P-value     Intercept   1959.709718   306.4905312   6.39403   1.21E-05     X1   -0.469657287   0.264557168   -1.77526   0.096144     X2   -2.163344882   0.278361425   -7.77171   1.23E-06     Using α = 0.01 to test the…

A multiple regression analysis produced the following tables. Summary Output                     Regression Statistics                     Multiple R   0.978724022                 R Square   0.957900711                         Adjusted R Square   0.952287472                 Standard Error   67.67055418                 Observations   18                                             ANOVA                         df   SS   MS   F   Significance F Regression   2   1562918.941   781459.5   170.6503   4.80907E-11 Residual   15   68689.55855   4579.304         Total   17   1631608.5                                       Coefficients   Standard Error   t Stat   P-value     Intercept   1959.709718   306.4905312   6.39403   1.21E-05     X1   -0.469657287   0.264557168   -1.77526   0.096144     X2   -2.163344882   0.278361425   -7.77171   1.23E-06     These results indicate that…

Analyse the following regression  Regression Statistics               Multiple R 0.79716916               R Square 0.63547867               Adjusted R Square 0.63254686               Standard Error 198.375358               Observations 377                                 ANOVA                   df SS MS F Significance F       Regression 3 25589530 8529843.34 216.753245 2.2501E-81       Residual 373 14678587.9 39352.7826           Total 376 40268117.9                                 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -97.981174 79.2847437 -1.2358137 0.21730553 -253.88228 57.9199296 -253.88228 57.9199296 EquivArea 3.5523258 0.15149067 23.4491399 2.2549E-75 3.254443 3.85020861 3.254443 3.85020861 Years 2.04629486 0.31770115 6.44094253 3.6636E-10 1.42158501 2.67100471 1.42158501 2.67100471 Condition 15.0379067 10.2144479 1.47221924 0.14180492 -5.0472147 35.1230282 -5.0472147 35.1230282

A sales manager for an advertising agency believes there is a relationship between the number of contacts that a salesperson makes and the amount of sales dollars earned. A regression ANOVA shows the following results:ANOVA   df SS MS F Significance F Regression 1.00 13,555.42 13,555.42 156.38 0.00 Residual 8.00 693.48 86.88     Total 9.00 14,248.90       What is the value of the standard error of estimate?   Multiple Choice   9.321   8.789   8.339   86.88

Analyse the following regression model Regression Statistics               Multiple R 0.7958395               R Square 0.63336051               Adjusted R Square 0.63139987               Standard Error 198.684728               Observations 377                                 ANOVA                   df SS MS F Significance F       Regression 2 25504235.6 12752117.8 323.037801 3.2564E-82       Residual 374 14763882.3 39475.6211           Total 376 40268117.9                                 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 6.10606259 35.9370414 0.16991 0.86517279 -64.557919 76.770044 -64.557919 76.770044 EquivArea 3.62347902 0.1437982 25.1983622 1.3118E-82 3.34072471 3.90623332 3.34072471 3.90623332 Years 1.90428034 0.30317478 6.28113036 9.3418E-10 1.30813952 2.50042116 1.30813952 2.50042116

WALKING RUNNING Mean 7.181818182 7.3636364 Variance 4.563636364 6.4545455 Observations 11 11 Pooled Variance 5.509090909   Hypothesized Mean Difference 0   df 20   t Stat -0.181668105   P(T<=t) one-tail 0.428835886   t Critical one-tail 1.724718243   P(T<=t) two-tail 0.857671772   t Critical two-tail 2.085963447   If the t-Stat value is less than the t Critical two-tail value I can reject the null, right? I am still a little confused on what a few numbers represent.

Based on the ANOVA table given, is there enough evidence at the 0.01 level of significance to conclude that the linear relationship between the independent variables and the dependent variable is statistically significant? ANOVA Source df MS F Significance F Regression 975.230086 487.615043 6.928784 0.009990 Residual 12 844.503247 70.375271 Total 14 1819.733333 Copy Data Answer 画 Tables 国 Keypad Keyboard Shortcuts O Yes O No

Based on the ANOVA table given, is there enough evidence at the 0.050.05 level of significance to conclude that the linear relationship between the independent variables and the dependent variable is statistically significant? ANOVA Source df SS MS F Significance F Regression 3 212.987150 70.995717 1.092713 0.448523 Residual 4 259.887850 64.971963     Total 7 472.875000

What is the R2?     What is the adjusted R2?     Explain the purpose of having an adjusted R2?

Mobiles Mean Standard Error 8200871.429 95839.39868 MOBILES Median 8211400 Mode |Standard Deviation Sample Variance #N/A 7,755,200 8,063,000 8,420,700 8,093,300 8,211,400 8,399,700 8,462,800 A B 253567.2147 64296332381 D Kurtosis 0.080691805 Skewness Range Minimum Maximum Sum Count E -0.80048705 F 707600 G 7755200 8462800 57406100 7

E2

Based on the ANOVA table given, is there enough evidence at the 0.05 level of significance to conclude that the linear relationship between the independent variables and the dependent variable is statistically significant? Source df SS Regression 2 984.715358 Residual 7 298.884642 Total 9 1283.600000 ANOVA MS F 492.357679 11.531217 42.697806 Significance F 0.006092 Copy Data Ĵ Based on the ANOVA table given, is there enough evidence at the 0.05 level of significance to conclude that the linear relationship between the independent variables and the dependent variable is statistically significant? Source df SS Regression 2 984.715358 Residual 7 298.884642 Total 9 1283.600000 ANOVA MS F Significance F 492.357679 11.531217 0.006092 42.697806 Copy Data

pre-test post-test Mean -2.25 0.75 Variance 9.30 13.30 T Observations 12 12 Pearson Correlation 0.9478 Hypothesized Mean 0 Difference df 11 t Stat 2.044 P(T<=t) one-tail 0.034 t Critical one-tail 1.796 P(T<=t) two-tail 0.066 t Critical two-tail 2.201 Based on the table above, which statement below is correct. a. The results of a paired t-test indicates no significant difference between conditions (t = 2.044, df = 11, p = 0.034) O b. The results of a two sample t-test indicates no significant difference between conditions (t = 2.044, df = 11, p = 0.034) O c. The results of a two sample t-test indicates no significant difference between conditions (t = 2.044, df = 11, p = 0.066) d. The results of a paired t-test indicates a significant difference between conditions (t = 2.044, df = 11, p = 0.066) e. The results of a two sample t-test indicates a significant difference between conditions (t = 2.044, df = 11, p = 0.034) f. The results of a paired t-test indicates a significant difference…

Female Variance s2= 3.266666429 Standard Deviation s= 1.8074 Coefficient of Variation cv= 2.93736%   Male Variance s2= 4.523809286 Standard Deviation s= 2.1269 Coefficient of Variation cv= 3.1588% Convert to z-score and use the areas under the normal curve table to answer the following: What percent (male and female) of your classmates are shorter than 5 feet? What percent (male and female) of your classmates are taller than 5 feet? What percent (male and female) of your classmates are shorter than 6 feet? What percent (male and female) of your classmates are between 60 inches and 70 inches?

What type of data is the dependent variable in a two sample t-test? Discrete Continuous Qualitative Binary

Refer to the ANOVA table for this regression.  Source SS d.f. MS Regression 1,164,578 5 232,916 Residual 1,500,689 45 33,349 Total 2,665,267 50    (a) State the degrees of freedom for the F test for overall significance.

Define how to use t-Statistic in Regression When the Sample Size Is Small? Explain Use of the Student t Distribution in Practice?

An educational psychologist is examining response times to an on-screen stimulus. The researcher believes there might be a weak effect from age, but expects a more pronounced effect for different color contrasts. She decides to examine a black on white (B/W) combination compared to 2 alternatives: red on white (R/W) and yellow on blue (Y/B). Here is the data for response times (in milliseconds): Color Scheme B/W R/W Y/B 10 17 23 26 44 44 22 21 25 22 21 4 16-17 11 32 34 31 24 20 16 20 21 36 8 8 24 37 23 11 39 36 23 17 28 32 36 31 Age 18-19 19 15 45 15 35 36 13 47 23 16 19 31 24 30 25 16 40 16 27 25 42 23 28 35 20-21 21 18 42 27 19 39 39 28 27 18 26 38 Using MS Excel, conduct a 2-way ANOVA with a = 0.05. Fill in the summary table. The last column in the table below is read as, "Partial Eta-squared " and is a concept that was NOT covered in the lectures. Partial n2 is used when there is a chance the data is not independent (click here to read more if interested 2) Computing the values for…

P-values should be accurate to 4 decimal places and all other values accurate to 3 decimal places. Source S df MS F-ratio P-value Partial n? Age (A) 2 Color (B) Interaction (A × B) 4 Error 63 Provide the conclusions for this 2-way ANOVA. What is the conclusion regarding the main effect for age (A or rows): O There appears to be an effect due to age. O There is not enough evidence to reject the assumption that the marginal means across age groups are equal. What is the conclusion regarding the main effect for color (B or columns): There appears to be an effect due to color. There is not enough evidence to reject the assumption that the marginal means across color groups are equal. What is the conclusion regarding an interaction effect between age and color (A x B): There appears to be an interaction effect between age and color. There is not enough evidence to reject the assumption of a lack of an interaction.

interpret this two way anova and multiple comparisons test with the data provided

determine if the frequency of deficiencies of variable (influenza/pneumococcal shots) are statistically significantly different between for-profit and non-profit nursing homes, interpret the results for the chi-square?