Practice for Module 9 (2)

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Salt Lake Community College *

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2020

Subject

Statistics

Date

Apr 3, 2024

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xlsx

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Uploaded by CountMagpiePerson722

Store A Store B Store C Store D Store E This is data for five groups, with 1 31.2 24.6 30.0 34.4 28.0 34.8 28.4 37.8 32.4 32.0 A. Use Data>Data Ana 26.0 31.8 31.4 36.0 28.8 yes B. At the 5% signfican 29.4 30.6 33.3 33.3 27.9 4.01 C. Which value of Q fr 33.9 34.6 38.7 29.9 25.7 10.177055 D. What is the Mean S 29.0 28.6 28.5 24.9 25.4 11.00 E. Appropriate value o 35.7 25.7 30.7 32.4 24.8 3.86 F. Critical Range 30.2 22.4 33.7 34.2 25.9 none Which stores, if any, c 29.3 24.3 31.7 34.8 26.2 CD Which stores, if any, c 27.3 26.6 29.5 27.9 32.0 30.7 27.2 33.1 32.5 24.0 Anova: Single Factor SUMMARY Groups Count Sum Average Variance Column 1 11 337.5 30.681818 9.2736364 Column 2 11 304.8 27.709091 12.884909 Column 3 11 358.4 32.581818 10.523636 Column 4 11 352.7 32.063636 10.828545 Column 5 11 300.7 27.336364 7.3745455 ANOVA urce of Variati SS df MS F P-value F crit Between Gr 260.75164 4 65.187909 6.4053807 0.000305 2.5571791 Within Grou 508.85273 50 10.177055 Total 769.60436 54

11 observations per group. alysis to produce ANOVA single factor results, and paste them beginning in cell A15 nce level, can you conclude that at least two of means are different? (Yes or no in the green box) rom the Q Table should be used in the critical range equation? 50 Square Within (MSW)? of "n" to use in the critical range equation can you conclude have a different mean than Store A? can you conclude have a different mean than Store B? Yes Anova: Single Factor 4.01 10.177055 SUMMARY 11 Groups Count Sum Average Variance 3.86 Store 1 11 337.5 30.68182 9.273636 none Store 2 11 304.8 27.70909 12.88491 CD Store 3 11 358.4 32.58182 10.52364 Store 4 11 352.7 32.06364 10.82855 Store 5 11 300.7 27.33636 7.374545 ANOVA Source of Varia SS df MS F P-value F crit Between G 260.7516 4 65.18791 6.405381 0.000305 2.557179 Within Gro 508.8527 50 10.17705 Total 769.6044 54

Number of Groups df↓ 3 4 5 6 7 8 10 3.88 4.33 4.66 4.92 5.13 5.31 11 3.82 4.26 4.58 4.83 5.03 5.21 12 3.78 4.20 4.51 4.75 4.95 5.12 13 3.74 4.16 4.46 4.69 4.89 5.05 14 3.71 4.12 4.41 4.64 4.83 4.99 15 3.68 4.08 4.37 4.60 4.79 4.94 16 3.65 4.05 4.34 4.56 4.75 4.90 17 3.63 4.02 4.31 4.53 4.71 4.86 18 3.61 4.00 4.28 4.50 4.68 4.83 19 3.60 3.98 4.26 4.47 4.65 4.80 20 3.58 3.96 4.24 4.45 4.62 4.77 21 3.57 3.95 4.22 4.43 4.60 4.75 22 3.56 3.93 4.20 4.41 4.58 4.73 23 3.55 3.92 4.18 4.39 4.56 4.71 24 3.54 3.91 4.17 4.38 4.55 4.69 25 3.53 3.89 4.16 4.36 4.53 4.67 26 3.52 3.88 4.15 4.35 4.52 4.66 27 3.51 3.87 4.13 4.34 4.50 4.64 28 3.50 3.87 4.12 4.33 4.49 4.63 29 3.50 3.86 4.12 4.32 4.48 4.62 30 3.49 3.85 4.11 4.31 4.47 4.61 35 3.47 3.82 4.07 4.27 4.43 4.56 40 3.45 3.80 4.04 4.24 4.39 4.53 45 3.43 3.78 4.02 4.21 4.37 4.50 50 3.42 3.76 4.01 4.19 4.35 4.48 55 3.41 3.75 3.99 4.18 4.33 4.46 60 3.40 3.74 3.98 4.17 4.32 4.45 70 3.39 3.73 3.96 4.15 4.30 4.42 80 3.38 3.72 3.95 4.13 4.28 4.41 90 3.37 3.71 3.94 4.12 4.27 4.39 200 3.34 3.66 3.89 4.07 4.21 4.33

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Observed excellent good fair poor Total male 58 99 42 20 219 female 41 91 59 36 227 Total 99 190 101 56 446 0.2219731 0.426009 0.2264574 0.1255605 Expected excellent good fair poor Male 48.6121 93.2960 49.5942 27.4978 Female 50.3879 96.7040 51.4058 28.5022 1.8130 0.3487 1.1629 2.0444 1.7491 0.3364 1.1219 1.9723 10.5487 0.01443 YES The data to the left shows product ra determine if there is a relationship b rating by answering the series of que A. Put the expected values in the gr B. Put the relative squared errors an green boxes to the left. C. Convert the value in cell F18 to a Excel formula. You must use the valu D. Can you conclude that there is a r and product rating? (Yes or no in the

Observed excellent good fair poor male 58 99 42 20 female 41 91 59 36 Total 99 190 101 56 0.221973 0.426009 0.226457 0.125561 Expected excellent good fair poor male 48.6121 93.2960 49.5942 27.4978 female 50.3879 96.7040 51.4058 28.5022 Relative Squared Errors excellent good fair poor male 1.8130 0.3487 1.1629 2.0444 female 1.7491 0.3364 1.1219 1.9723 TOTAL: rating by gender. Test to between gender and product estions below. reen boxes to the left. nd total of these errors in the p-value using the appropriate ue in cell F18. relationship between gender e box).

Total 219 227 446 10.5487 0.01443 Yes

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Y, sales X, Advertising 664 1200 684 1400 614 1500 537 1500 693 1500 603 1000 424 1000 468 1300 591 1200 985 2000 802 1600 887 1700 a 15.41+0.46X <<The equation that relates sales (Y) to advertising (X). Round to the b 3.25 c d 0 e 66.14% f YES g P20 a Y = 15.41 + 0.46X b 842.67 << If you have 843.41, you used rounded values from (a). This will be marked as incor c 46.0 d 0.813 <<Note these rounding and format instructions. They are the same on the assignmen e 66.14% <<Note these rounding and format instructions. They are the same on the assignmen f 0.855 <<Predict sales when $1800 is spent on advertising. Use cell referenc rather than the rounded values from part (a). <<Raising advertising by $100 increases sales by how much? You ma hundredth if you wish. <<What is the correlation coefficient for this model? (Express as a de places) <<What percentage of the variation in sales is explained by advertisi rounded to two decimal places) <<Is there a statistically significant correlation between the variables No) <<Which cell of your regression result (for example, "L9") contains th value you used to answer the previous question? You believe that the number of units you sell per month depends on advertising. You have collected below for 12 months. Run a regression on this data using Sales as the Y variable, and Advertising as the X variable. Have Ex the output at cell L3. Then, fill in the information requested below in the green boxes.

g Yes h P20 (if you set the results to begin in L3)

SUMMARY OUTPUT Regression Statistics Multiple R 0.813271 R Square 0.661409 Adjusted R 0.62755 Standard E 100.2345 Observatio 12 ANOVA df SS MS F Regression 1 196259.2 196259.2 19.5342 Residual 10 100469.5 10046.95 e nearest hundredth. Total 11 296728.7 Coefficients andard Erro t Stat P-value Intercept 15.41435 149.2766 0.10326 0.919798 X, Advertis 0.459587 0.103985 4.419751 0.001295 0.001295 45.95874 SUMMARY OUTPUT rrect on the assignment. Regression Statistics nt. Multiple R 0.813271 nt. R Square 0.661409 Adjusted R 0.62755 ces from the coefficients column ay round to the nearest ecimal rounded to three decimal ing? (Express as a percent s at the 5% level? (Enter Yes or he numerical d the data xcel begin

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Standard E 100.2345 Observatio 12 ANOVA df SS MS F ignificance Regression 1 196259.2 196259.2 19.5342 0.001295 Residual 10 100469.5 10046.95 Total 11 296728.7 Coefficients andard Erro t Stat P-value Lower 95% Intercept 15.41435 149.2766 0.10326 0.919798 -317.1947 X, Advertis 0.459587 0.103985 4.419751 0.001295 0.227895

ignificance F 0.001295 Lower 95%Upper 95% Lower 95.0% Upper 95.0%Upper 95% Lower 95.0% Upper 95.0% -317.1947 348.0234 -317.1947 348.0234 348.0234 -317.1947 348.0234 0.227895 0.69128 0.227895 0.69128 0.69128 0.227895 0.69128

F Upper 95% Lower 95.0% Upper 95.0% 348.0234 -317.1947 348.0234 0.69128 0.227895 0.69128

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Y, sales X, Price $ 567 105 587 110 250 120 440 125 596 100 506 95 695 100 371 115 494 120 888 100 705 110 790 108 a Y = 1783.96-11.10X <<The equation that relates sales (Y) to price (X). Round to the neare b 0.00 c d 34.63% e f g a Y = 1783.96 - 11.10X b 562.98 << If you have 562.96, you used rounded values from (a). This will be marked as incor c 11.10 d 34.63% <<Note these rounding and format instructions. They are the same on the assignmen e -0.588 <<Note these rounding and format instructions. They are the same on the assignmen <<Predict sales at a price of $110. Use cell references from the coeffi rounded values from part (a). <<Raising price by $1 decreases sales by how much? You may round wish. <<What percentage of the variation in sales is explained by price? (E two decimal places) <<What is the correlation coefficient for this model? (Express as a de places) <<Is there a statistically significant correlation between the variables No) <<Which cell of your regression result (for example, "L9") contains th value you used to answer the previous question? You believe that the number of units you sell per month depends on the price you charge. You have the data below for 12 months. Run a regression on this data using Sales as the Y variable, and Price as the X variable. Have Excel be output at cell L3. Then, fill in the information requested below in the green boxes.

f Yes g P20 (if you set the results to begin in L3)

SUMMARY OUTPUT Regression Statistics Multiple R 0.588481 R Square 0.34631 Adjusted R 0.28094 Standard E 151.8885 Observatio 12 ANOVA df SS MS F Regression 1 122219.9 122219.9 5.29776 Residual 10 230701 23070.1 est hundredth. Total 11 352920.9 Coefficients andard Erro t Stat P-value Intercept 1783.961 527.474 3.382084 0.006978 X, Price $ -11.0998 4.822463 -2.301686 0.044127 11.0998 rrect on the assignment. SUMMARY OUTPUT nt. Regression Statistics nt. Multiple R 0.588481 fficients column rather than the d to the nearest hundredth if you Express as a percent rounded to ecimal rounded to three decimal s at the 5% level? (Enter Yes or he numerical collected egin the

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R Square 0.34631 Adjusted R 0.28094 Standard E 151.8885 Observatio 12 ANOVA df SS MS F Regression 1 122219.9 122219.9 5.29776 Residual 10 230701 23070.1 Total 11 352920.9 Coefficients andard Erro t Stat P-value Intercept 1783.961 527.474 3.382084 0.006978 X, Price $ -11.0998 4.822463 -2.301686 0.044127

ignificance F 0.044127 Lower 95%Upper 95% Lower 95.0% Upper 95.0% 608.676 2959.247 608.676 2959.247 -21.84492 -0.35468 -21.84492 -0.35468

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Practice for Module 9 (2)

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