## I need this in 2 days the is an excel assignment

Objectives:

Create 3 Pivot Tables with Analysis ChartsCreate Conditional Formatting for easy viewing of key informationApply Sort and Filters to data setPresent a professionally formatted report with custom formatting

Part 1: Pivot Tables and Charts

Using the data file:

• DAR Assignment 3 Data3.xlsx

120 KB

Create a total of three (3) pivot tables on separate worksheets with the following data fields:

1. Sum of Revenue by Store and Region

2. Sum of COGS by Store and Region

3. Sum of Profit by Store and Region

Sample of the basic format of the pivot table (5×5 box, numbers will be different)

LINK TO PART 1 OF VIDEO: https://www.youtube.com/watch?v=rx1LeeW76AU

Design a properly formatted chart for

each

pivot table with a directed point of view chart title, axis titles, and accurate chart type.

Part 2: Conditional Formatting

Copy the data sheet provided into a new worksheet within the same workbookOn the copied worksheet, apply two different custom conditional formats in the COGS (Cost of Goods Sold) column.

Criteria to include:

• Top 10% COGS figures as one format

• Bottom 10% COGS figures as a second format

• Format should be unique/customized and NOT one of the defaults set by Excel

Apply another Conditional Format in the Regions Column.

Criteria to include:

• Region contains the word “East”

• Format should be unique/customized and NOT one of the defaults set by Excel

LINK TO PART 2 OF THE VIDEO: https://www.youtube.com/watch?v=ydNAQzY3I_A

Apply all worksheet design guidelines to all worksheets*

*

Note:

It is important to do Currency/Accounting formatting prior to Part 3 or all of your figures may not format properly due to the filter

Part 3: Sort and Filter

Using the same worksheet that you have applied conditional formatting to:

• Sort the data with the criteria of Revenues as

Largest to Smallest

• Apply Filter#1 in the Profits Column to show only the

Top 25 Profit stores

(show items, not %)

• Apply Filter#2 in the Region Column to show only data in the

East Region

Adjust sizing of rows and columns as needed to be able to read all data presented.

Note: your final sheet may show less than 25 stores in the filter due to the top 25 having other locations such as West or Central.  You will not be able to see all of your conditional formats on the screen (bottom % for example) due to the filter; however, they are still applied in the background if created correctly in Step 2.

THIS IS AN EXCEL ASSIGNMENT BEFORE AGREE TO ASSIGNMENT PLEASE MAKE SURE YOU ARE GOOD IN EXCEL. THANK YOU….

PLEASE MAKE SURE YOU READ THE DIRECTIONS CAREFULLY AND LOOK AT THE VIDEOS AND THE ATTACHMENTS.

## ANOVA

See the posted sample SAS Univ_TTest_ANOVA_REG program, HBAT-tabs dataset, and SAS Univ_TTest_ANOVA_REG output, for the analysis of X3-Firm Size along with X19-Satisfaction and X20-Likelihood of Recommendation.

Now perform a similar statistical data analysis using X5-Distribution System as the Class Independent predictor variable and X19-Satisfaction and X20-Likelihood of Recommendation as the Dependent response variables, including assessment of testing the differences of X19-Satisfaction and X20-Likelihood of Recommendation across levels of X5-Distribution System.

Provide a brief 3/4-1 page summary along with the program and output as an appendix.

ANOVA
SAS Output HBAT_Univ_TTest_ANOVA_REG_Prog_SAS_Output_Fa16.htm[9/26/2016 9:00:05 PM] HBAT – Two-Sample T-Test ObsIDX1 X2X3X4X5 X6X7X8X9X10 X11X12X13X14X15X16X17X18X19X20X21 X22X23 1 1201118.5 3.92.55.9 4.84.96.06.84.74.35.05.13.78.28.08.465.1 1 2 2310008.2 2.75.17.2 3.47.93.15.35.54.03.94.34.95.76.57.567.1 0 3 3301119.2 3.45.65.6 5.47.45.84.56.24.65.44.04.58.98.49.072.1 1 4 4111106.4 3.37.03.7 4.74.74.58.87.03.64.34.13.04.86.07.240.1 0 5 5201019.0 3.45.24.6 2.26.04.56.86.14.54.53.53.57.16.69.057.1 0 6 6110106.5 2.83.14.1 4.04.33.78.55.19.53.64.73.34.76.36.150.1 0 7 7111106.9 3.75.02.6 2.12.35.48.94.82.52.14.22.05.77.87.241.1 0 8 8201106.2 3.33.94.8 4.63.65.16.95.44.84.36.33.76.35.87.756.1 0 9 9211105.8 3.65.16.7 3.75.95.89.35.94.44.46.14.67.07.58.256.1 1 10 10 101106.4 4.55.16.1 4.75.75.78.45.45.34.15.84.45.55.96.759.1 0 11 11 301018.7 3.24.64.8 2.76.84.66.85.87.53.83.74.07.47.08.468.1 0 12 12 101106.1 4.96.33.9 4.43.96.48.25.85.93.04.93.26.06.36.653.1 0 13 13 110019.5 5.64.66.9 5.06.96.67.66.55.35.14.54.48.48.47.958.1 1 14 14 310019.2 3.95.75.5 2.48.44.87.16.73.04.52.64.27.66.98.272.1 1 15 15 201116.3 4.54.76.9 4.56.85.98.86.05.44.86.25.28.07.07.662.1 1 16 16 300008.7 3.24.06.8 3.27.83.84.96.15.04.33.94.56.66.47.171.1 0 17 17 210115.7 4.06.76.0 3.35.55.16.26.75.44.26.24.56.47.57.250.1 1 18 18 201105.9 4.15.57.2 3.56.45.58.46.26.35.75.84.87.46.98.258.1 1 19 19 211105.6 3.45.16.4 3.75.75.69.15.46.15.06.04.56.87.57.955.1 0 20 20 301109.1 4.53.66.4 5.35.37.18.45.86.74.56.14.47.68.58.867.1 1 21 21 100105.2 3.87.15.2 3.94.35.08.47.14.63.34.93.35.45.57.050.1 0 22 22 311119.6 5.76.85.9 5.48.37.84.56.46.54.33.04.39.99.69.970.1 1 23 23 200018.6 3.67.45.1 3.57.34.73.76.76.04.83.44.07.07.18.160.1 0 24 24 301119.3 2.42.67.2 2.27.24.56.26.44.26.74.44.58.68.18.065.1 1 25 25 100106.0 4.15.34.7 3.55.35.38.06.53.94.75.34.04.84.95.555.1 0 26 26 201106.4 3.66.66.1 4.03.95.37.16.13.75.66.63.96.66.87.058.1 0 27 27 300008.5 3.07.25.8 4.17.63.74.86.96.75.33.84.46.37.17.070.1 0 28 28 110107.0 3.35.45.5 2.64.84.29.06.55.94.35.23.75.45.55.655.1 0 29 29 300008.5 3.05.76.0 2.37.63.74.85.86.05.73.84.46.36.97.270.1 0 30 30 111107.6 3.63.04.0 5.14.24.67.74.97.24.75.53.55.45.56.252.1 0 31 31 110016.9 3.48.54.3 4.56.44.75.27.73.33.72.73.36.16.87.144.1 0 32 32 101108.1 2.57.24.5 2.35.13.86.66.86.13.03.53.06.45.86.251.1 0 33 33 111106.7 3.76.55.3 5.35.14.99.25.74.23.54.53.45.46.57.644.1 0 34 34 211108.0 3.36.15.7 5.54.64.78.75.93.84.76.64.27.37.59.062.1 1 35 35 101106.7 4.05.23.9 3.05.46.88.46.26.02.54.33.56.36.66.754.1 0 36 36 100008.7 3.26.14.3 3.56.12.95.66.16.53.12.92.55.44.67.151.1 0 37 37 200019.0 3.45.94.6 3.96.04.56.86.44.33.93.53.57.18.07.257.1 0 38 38 301119.6 4.16.27.3 2.97.75.57.76.14.45.24.64.98.79.99.977.1 1 39 39 211108.2 3.63.96.2 5.84.95.09.05.27.14.76.94.57.66.97.665.1 1 40 40 100106.1 4.93.04.8 5.13.96.48.25.16.84.54.93.26.05.55.853.1 0 41 41 211108.3 3.43.35.5 3.14.65.29.14.11.74.65.83.97.07.58.461.1 1 SAS Output HBAT_Univ_TTest_ANOVA_REG_Prog_SAS_Output_Fa16.htm[9/26/2016 9:00:05 PM] 4242 210019.4 3.84.75.4 3.86.54.98.54.96.24.14.54.17.68.07.961.1 1 4343 301019.3 5.14.66.8 5.86.66.37.45.14.14.64.64.38.97.87.672.1 1 44 44 211115.1 5.16.66.9 4.45.47.85.97.25.24.96.34.57.67.98.455.1 1 45 45 310008.0 2.54.77.1 3.67.73.05.25.13.94.34.24.75.55.66.565.1 0 46 46 201105.9 4.15.75.9 5.86.45.58.46.45.15.25.84.87.48.67.758.1 1 47 47 3100110.0 4.37.16.3 2.95.44.53.86.73.75.04.03.57.18.88.067.1 1 48 48 211105.7 3.86.87.5 5.75.76.08.26.64.86.57.35.27.67.67.160.1 0 49 49 300119.9 3.73.76.1 4.27.06.76.85.97.24.53.43.98.78.18.567.1 1 50 50 311017.9 3.94.35.8 4.46.95.84.75.23.64.14.24.38.67.87.661.1 1 51 51 101106.7 3.65.94.2 3.44.74.87.25.75.34.03.62.85.47.57.248.1 0 52 52 310008.2 2.73.77.4 2.77.93.15.35.35.04.54.34.95.77.18.267.1 1 53 53 301119.4 2.54.86.1 3.27.34.66.36.39.24.74.64.68.79.09.066.1 1 54 54 110016.9 3.45.74.4 3.36.44.75.26.44.43.22.73.36.17.07.244.1 0 55 55 211108.0 3.33.85.8 3.24.64.78.75.34.24.96.64.27.38.18.162.1 1 56 56 310009.3 3.87.35.7 3.76.45.57.46.65.94.13.23.47.77.68.959.1 1 57 57 201117.4 5.14.87.7 4.57.26.99.66.47.45.76.55.59.07.98.874.1 1 58 58 310007.6 3.65.25.8 5.66.65.44.46.76.44.63.94.08.27.57.558.1 1 5959 3100010.0 4.35.33.7 4.25.44.53.86.74.53.74.03.57.16.57.067.1 0 60 60 311109.9 2.87.26.9 2.65.83.55.46.27.05.64.94.07.98.58.561.1 1 61 61 300008.7 3.28.46.1 2.87.83.84.97.24.55.43.94.56.66.97.271.1 1 62 62 201118.4 3.86.75.0 4.54.75.96.75.14.22.75.03.68.07.68.863.1 1 63 63 100018.8 3.93.85.1 4.34.74.85.85.07.24.43.72.96.35.58.044.1 0 64 64 101107.7 2.26.34.5 2.44.73.46.26.04.73.33.12.66.06.08.147.1 0 65 65 101106.6 3.65.84.1 4.94.74.87.26.53.93.53.62.85.46.97.148.1 0 66 66 211105.7 3.83.56.7 5.45.76.08.25.45.04.77.35.27.66.99.060.1 1 67 67 210105.7 4.07.96.4 2.75.55.16.27.56.45.06.24.56.45.66.250.1 0 68 68 210115.5 3.74.75.4 4.35.34.96.05.62.54.55.94.36.16.38.248.1 0 69 69 111107.5 3.53.83.5 2.94.14.57.65.15.24.05.43.45.25.85.851.1 0 70 70 201106.4 3.62.75.3 3.93.95.37.15.25.54.76.63.96.66.68.058.1 1 71 71 300109.1 4.56.15.9 6.35.37.18.47.15.75.46.14.47.67.57.767.1 0 72 72 110016.7 3.23.03.7 4.86.34.55.05.22.52.92.63.15.86.07.043.1 0 73 73 201106.5 4.32.76.6 6.56.36.08.74.76.34.65.64.67.96.67.966.1 0 74 74 301119.9 3.77.54.7 5.67.06.76.87.24.64.13.43.98.68.89.866.1 1 7575 201118.5 3.95.35.5 5.04.96.06.85.73.64.45.13.78.27.08.465.1 1 76 76 300009.9 3.06.85.0 5.45.94.84.97.37.63.14.33.87.16.68.963.1 1 77 77 100117.6 3.67.64.6 4.74.65.07.48.16.64.55.83.96.46.97.549.1 0 78 78 210019.4 3.87.06.2 4.76.54.98.57.32.44.34.54.17.67.38.061.1 1 79 79 300019.3 3.56.37.6 5.57.55.94.66.63.15.24.14.68.97.38.172.1 1 80 80 111107.1 3.44.94.1 4.05.05.97.86.13.52.63.12.75.75.87.644.1 0 81 81 301009.9 3.07.44.8 4.05.94.84.95.96.93.24.33.87.17.98.863.1 0 82 82 300008.7 3.26.44.9 2.46.84.66.86.35.14.33.74.07.47.38.068.1 1 83 83 200018.6 2.95.83.9 2.95.64.06.36.14.02.73.03.06.66.18.553.1 0 84 84 110106.4 3.26.73.6 2.22.95.08.47.36.52.03.71.65.05.16.537.1 0 85 85 200017.7 2.66.76.6 1.97.24.35.96.54.14.73.94.38.27.57.752.1 1 SAS Output HBAT_Univ_TTest_ANOVA_REG_Prog_SAS_Output_Fa16.htm[9/26/2016 9:00:05 PM] 8686 111107.5 3.54.14.5 3.54.14.57.64.92.83.45.43.45.26.07.251.1 0 8787 100105.0 3.61.33.0 3.54.24.98.24.37.62.44.83.15.25.56.048.1 0 88 88 200017.7 2.68.06.7 3.57.24.35.96.97.75.13.94.38.27.68.252.1 0 89 89 210019.1 3.65.55.4 4.26.24.68.36.54.14.64.33.97.36.57.459.1 0 90 90 210115.5 5.57.77.0 5.65.78.26.37.44.95.56.74.98.27.69.359.1 1 91 91 310009.1 3.77.04.1 4.46.35.47.37.54.64.43.03.37.47.97.958.1 1 92 92 110107.1 4.24.12.6 2.13.34.59.95.53.52.04.02.44.85.06.551.1 0 93 93 311019.2 3.94.65.3 4.28.44.87.16.26.64.42.64.27.67.58.672.1 0 94 94 301119.3 3.55.47.8 4.67.55.94.66.44.94.84.14.68.97.68.972.1 1 95 95 311009.3 3.84.04.6 4.76.45.57.45.34.83.63.23.47.77.38.459.1 1 96 96 110018.6 4.85.65.3 2.36.05.76.75.83.64.93.63.67.38.18.150.1 1 97 97 100117.4 3.42.65.0 4.14.44.87.24.56.44.25.63.76.35.57.248.1 0 98 98 100018.7 3.23.33.2 3.16.12.95.65.04.33.12.92.55.47.07.751.1 0 99 99 210117.8 4.95.85.3 5.25.37.17.96.05.74.34.93.96.47.17.461.1 0 100 100 211107.9 3.04.45.1 5.94.24.89.75.75.83.45.43.56.47.37.057.1 0 SAS Output HBAT_Univ_TTest_ANOVA_REG_Prog_SAS_Output_Fa16.htm[9/26/2016 9:00:05 PM] HBAT – Two-Sample T-Test The UNIVARIATE Procedure Variable: X19 (X19 – Satisfaction) Moments N 100Sum Weights 100 Mean 6.918Sum Observations 691.8 Std Deviation 1.19183925Variance 1.42048081 Skewness 0.0781812Kurtosis -0.7913045 Uncorrected SS 4926.5Corrected SS 140.6276 Coeff Variation 17.2280898Std Error Mean 0.11918393 Basic Statistical Measures Location Variability Mean 6.918000 Std Deviation 1.19184 Median 7.050000 Variance 1.42048 Mode 7.600000 Range 5.20000 SAS Output HBAT_Univ_TTest_ANOVA_REG_Prog_SAS_Output_Fa16.htm[9/26/2016 9:00:05 PM] 5.084 9.922 SAS Output HBAT_Univ_TTest_ANOVA_REG_Prog_SAS_Output_Fa16.htm[9/26/2016 9:00:05 PM] HBAT – Two-Sample T-Test The UNIVARIATE Procedure Variable: X20 (X20 – Likelihood of Recommendation) Moments N 100Sum Weights 100 Mean 7.02Sum Observations 702 Std Deviation 1.04330477Variance 1.08848485 Skewness 0.04392529Kurtosis -0.0883467 Uncorrected SS 5035.8Corrected SS 107.76 Coeff Variation 14.8618913Std Error Mean 0.10433048 Basic Statistical Measures Location Variability Mean 7.020000 Std Deviation 1.04330 Median 7.000000 Variance 1.08848 Mode 7.500000 Range 5.30000 SAS Output HBAT_Univ_TTest_ANOVA_REG_Prog_SAS_Output_Fa16.htm[9/26/2016 9:00:05 PM] 5.597 9.938 SAS Output HBAT_Univ_TTest_ANOVA_REG_Prog_SAS_Output_Fa16.htm[9/26/2016 9:00:05 PM] HBAT – Two-Sample T-Test The TTEST Procedure Variable: X19 (X19 – Satisfaction) X3 NMean Std Dev Std ErrMinimum Maximum 0 496.6408 1.09160.1559 4.7000 8.9000 1 517.1843 1.23330.1727 4.8000 9.9000 Diff (1-2) -0.5435 1.16600.2333 X3 Method Mean95% CL Mean Std Dev95% CL Std Dev 0 6.64086.32736.9544 1.09160.91031.3638 1 7.18436.83757.5312 1.23331.03191.5330 Diff (1-2) Pooled -0.5435-1.0064-0.0806 1.16601.02321.3557 Diff (1-2) Satterthwaite -0.5435-1.0053-0.0817 Method Variances DFt Value Pr > |t| Pooled Equal 98-2.33 0.0219 Satterthwaite Unequal97.357-2.340.0216 Equality of Variances Method Num DFDen DFF Value Pr > F Folded F 50481.280.3977 SAS Output HBAT_Univ_TTest_ANOVA_REG_Prog_SAS_Output_Fa16.htm[9/26/2016 9:00:05 PM] Variable: X20 (X20 – Likelihood of Recommendation) X3 NMean Std Dev Std ErrMinimum Maximum 0 496.7306 1.00630.1438 4.6000 8.8000 1 517.2980 1.01100.1416 5.5000 9.9000 Diff (1-2) -0.5674 1.00870.2018 X3 Method Mean95% CL Mean Std Dev95% CL Std Dev 0 6.73066.44167.0196 1.00630.83921.2571 1 7.29807.01377.5824 1.01100.84591.2568 Diff (1-2) Pooled -0.5674-0.9679-0.1670 1.00870.88511.1728 Diff (1-2) Satterthwaite -0.5674-0.9678-0.1670 Method Variances DFt Value Pr > |t| Pooled Equal 98-2.81 0.0059 Satterthwaite Unequal97.875-2.810.0059 Equality of Variances Method Num DFDen DFF Value Pr > F Folded F 50481.010.9753 SAS Output HBAT_Univ_TTest_ANOVA_REG_Prog_SAS_Output_Fa16.htm[9/26/2016 9:00:05 PM] SAS Output HBAT_Univ_TTest_ANOVA_REG_Prog_SAS_Output_Fa16.htm[9/26/2016 9:00:05 PM] HBAT – Two-Sample T-Test The ANOVA Procedure Class Level Information Class Levels Values X3 20 1 Number of Observations Read 100 Number of Observations Used 100 SAS Output HBAT_Univ_TTest_ANOVA_REG_Prog_SAS_Output_Fa16.htm[9/26/2016 9:00:05 PM] HBAT – Two-Sample T-Test The ANOVA Procedure Dependent Variable: X19 X19 – Satisfaction SourceDFSum of Squares Mean SquareF ValuePr > F Model 17.3817817 7.3817817 5.430.0219 Error 98133.2458183 1.3596512 Corrected Total 99140.6276000 R-Square Coeff Var Root MSE X19 Mean 0.052492 16.85517 1.1660416.918000 Source DFAnova SS Mean Square F ValuePr > F X3 17.38178167 7.38178167 5.430.0219 SAS Output HBAT_Univ_TTest_ANOVA_REG_Prog_SAS_Output_Fa16.htm[9/26/2016 9:00:05 PM] HBAT – Two-Sample T-Test The ANOVA Procedure Class Level Information Class Levels Values X3 20 1 Number of Observations Read 100 Number of Observations Used 100 SAS Output HBAT_Univ_TTest_ANOVA_REG_Prog_SAS_Output_Fa16.htm[9/26/2016 9:00:05 PM] HBAT – Two-Sample T-Test The ANOVA Procedure Dependent Variable: X20 X20 – Likelihood of Recommendation Source DFSum of Squares Mean SquareF ValuePr > F Model 18.0461144 8.0461144 7.910.0059 Error 9899.7138856 1.0174886 Corrected Total 99107.7600000 R-Square Coeff Var Root MSE X20 Mean 0.074667 14.36904 1.0087067.020000 Source DFAnova SS Mean Square F ValuePr > F X3 18.04611445 8.04611445 7.910.0059 SAS Output HBAT_Univ_TTest_ANOVA_REG_Prog_SAS_Output_Fa16.htm[9/26/2016 9:00:05 PM] HBAT – Two-Sample T-Test The REG Procedure Model: MODEL1 Dependent Variable: X19 X19 – Satisfaction Number of Observations Read 100 Number of Observations Used 100 Analysis of Variance Source DFSum of Squares Mean Square F Value Pr SAS Output HBAT_Univ_TTest_ANOVA_REG_Prog_SAS_Output_Fa16.htm[9/26/2016 9:00:05 PM] HBAT – Two-Sample T-Test The REG Procedure Model: MODEL1 Dependent Variable: X19 X19 – Satisfaction SAS Output HBAT_Univ_TTest_ANOVA_REG_Prog_SAS_Output_Fa16.htm[9/26/2016 9:00:05 PM] SAS Output HBAT_Univ_TTest_ANOVA_REG_Prog_SAS_Output_Fa16.htm[9/26/2016 9:00:05 PM] SAS Output HBAT_Univ_TTest_ANOVA_REG_Prog_SAS_Output_Fa16.htm[9/26/2016 9:00:05 PM] HBAT – Two-Sample T-Test The REG Procedure Model: MODEL1 Dependent Variable: X20 X20 – Likelihood of Recommendation Number of Observations Read 100 Number of Observations Used 100 Analysis of Variance Source DFSum of Squares Mean Square F Value Pr SAS Output HBAT_Univ_TTest_ANOVA_REG_Prog_SAS_Output_Fa16.htm[9/26/2016 9:00:05 PM] HBAT – Two-Sample T-Test The REG Procedure Model: MODEL1 Dependent Variable: X20 X20 – Likelihood of Recommendation SAS Output HBAT_Univ_TTest_ANOVA_REG_Prog_SAS_Output_Fa16.htm[9/26/2016 9:00:05 PM] SAS Output HBAT_Univ_TTest_ANOVA_REG_Prog_SAS_Output_Fa16.htm[9/26/2016 9:00:05 PM]
ANOVA
*; *; * HBAT – Two -Sample T -Test and Univariate Examples; *; *; ods graphics on ; *; options ls =80 ps =50 nodate pageno= 1; *; Title ‘HBAT – Two -Sample T -Test’ ; *; * Input HBAT ; *; Data HBAT; Infile ‘N: BIA652D_Multivariate Data Analytics_2016_Fall Class_04_Chap 2HBAT_tabs.txt’ DLM = ’09’X TRUNCOVER ; Input ID X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 X20 X21 X22 X23; *; Data HBAT; Set HBAT (Keep = ID X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 X20 X21 X22 X23); Label ID = ‘ID – Identification Number’ X1 = ‘X1 – Customer Type’ X2 = ‘X2 – Industry Type’ X3 = ‘X3 – Firm Size’ X4 = ‘X4 – Region’ X5 = ‘X5 – Distribution System’ X6 = ‘X6 – Product Quality’ X7 = ‘X7 – E-Commerce’ X8 = ‘X8 – Technical Support’ X9 = ‘X9 – Complaint Resolution’ X10 = ‘X10 – Advertizing’ X11 = ‘X11 – Product Line’ X12 = ‘X12 – Salesforce Image’ X13 = ‘X13 – Competitive Pricing’ X14 = ‘X14 – Warranty & Claims’ X15 = ‘X15 – New Products’ X16 = ‘X16 – Order & Billing’ X17 = ‘X17 – Price Flexibility’ X18 = ‘X18 – Delivery Speed’ X19 = ‘X19 – Satis faction’ X20 = ‘X20 – Likelihood of Recommendation’ X21 = ‘X21 – Likelihood of Future Purchase’ X22 = ‘X22 – Current Purchase/Usage Level’ X23 = ‘X23 – Consider Strategic Alliance/Partnership in Future’ ; *; *; Data HBAT; Set HBAT; *; Proc Print Data = HBAT; *; * HBAT – Univariate; *; Proc Univariate Data = HBAT; Var X19 X20; *; * HBAT – Two -Sample T -Test; *; Proc TTest Data = HBAT; Class X3; Var X19 X20; *; *; * HBAT – ANOVA; *; Proc ANOVA Data = HBAT; Class X3; Model X19 = X3; *; Proc ANOVA Data = HBAT; Class X3; Model X20 = X3; *; *; * HBAT – REG; *; Proc REG Data = HBAT; Model X19 = X3; *; Proc REG Data = HBAT; Model X20 = X3; *; * ods graphics off; *; *; Run ; Quit ;
ANOVA
SAS Univariate_TTest_ANOVA_REG – HW Due June 13th See the posted sample SAS Univ_TTest_ANOVA_REG program, HBAT-tabs dataset, and SAS Univ_TTest_ANOVA_REG output, for the analysis of X3-Firm Size along with X19-Satisfaction and X20-Likelihood of Recommendation. Now perform a similar statistical data analysis using X5-Distribution System as the Class Independent predictor variable and X19-Satisfaction and X20-Likelihood of Recommendation as the Dependent response variables, including assessment of testing the differences of X19-Satisfaction and X20-Likelihood of Recommendation across levels of X5-Distribution System. Provide a brief 3/4-1 page summary along with the program and output as an appendix.

## DUE IN 4 HOURS

discussion ONE PAGE

Watch the following video http://www.ted.com/talks/margaret_wertheim_crochets_the_coral_reef.htmlThe idea of a hyperbolic space is, as the speaker said, about anything that grows so much at the edges that it becomes ‘curly’ or ‘wavy’.  Give an example of an object that you suspect might be a hyperbolic space in this way

## Response to Another Student Discussion on tests and measurements

Directions are attached. This is 1 of 2 responses that will be required. Please follow directions explicitly and cite all references

Response to Another Student Discussion on tests and measurements

## Response to another student discussion: Odds in logistic regression

Respond to another student discussion on odds in logistic regression.Respond  by evaluating the learner’s response. Do you agree or disagree? Why? Do you consider this a good answer to the question? Why or why not?

Please cite references. Student discussion is attached.

Response to another student discussion: Odds in logistic regression

## f(x)=2x

f(x)=2x

Answer Submitted by Coloratus on Fri, 2012-01-06 06:59teacher rated 77 times 4.753245price: \$0.00 Linear function

f(x) = 2x

is a form of linear function, generally: f(x) = ax + b

In this case b = 0, meaning that the line (graph of this function) goes through the origin of coordinate system.

a = 2, meaning that the slope of the line is 2, or the angle between the line and +x is bigger than 45°. The function is always rising.

We can draw graph if we know 2 points and draw a line between them:

f(0) = 0

f(1) = 2

So, the points are: (0, 0), (1, 2), and the graph is on the picture

## Exercise 10

Complete Exercise 10 in Statistics for Nursing Research: A Workbook for Evidence-Based Practice, and submit as directed by the instructor.

Exercise 10

## Response to Another Student Rotation Discussion

DIRECTIONS: READ THE ATTACHED DISCUSSION BELOW BY ANOTHER STUDENT AND:

Respond by expanding on one of the points made. Cite references.

Response to Another Student Rotation Discussion

## EX18

Complete Exercise 18 in Statistics for Nursing Research: A Workbook for Evidence-Based Practice, and submit as directed by the instructor

EX18

## Inferential Statistics and Findings

Complete

an inferential statistics (hypothesis) test usingthe research question and variables your learning team developed for the Week 2 Business Research Project Part 1 assignment. Include:

• The research question (Most up to date version using team all team discussion and instructor feedback)
• Mock data for the independent and dependent variables (from Week 4)

Determine

the appropriate statistical tool to test the hypothesis based on the research question. (t-test, ANOVA, Chi-Squared test, test of correlation coefficient, etc)

Conduct

a hypothesis test with a 0.05 level of significance.

Write

an interpretation of no more than 350-words about the results of the hypothesis test.  Clearly connect the results of the test to your Research Question (RQ).

Include

the Excel output or test calculations in an Appendix.