Answer Happy

Can you help me with this? The instruction in these pictures.
Please email me the excel and everything that is associated with
this problem. Ethanzhou63 AT G m ai l.com
Math 324 lab 1 The goal of this lab is to analyze the distribution of data in a random sample selected from a parent population and compare it to the distribution of data in the population itself. The instructions are stated in terms of Excel, however, you may use any program of your choice (report which one). Follow the attached implementation prototype. First, the Data Analysis add-in must be added to your Excel version for all our labs. For that use the Manage Add-ins option from the Excel Options menu and check the Analysis Toolpak and Analysis Toolpak - VBA lines. 1. Use the Random Generation tool in the Data Analysis toolkit to generate 30 random numbers using the "Normal" probability distribution with the mean mu = 20 and standard deviation sigma = 5, and put them in the range, say, A2:A31. Put a data label in A1. Use the Sort functions to put the data in ascending order. 2. Use the Descriptive Statistics tool in the Data Analysis toolkit to produce a report on Summary Statistics of the generated data and place the report in the range, say, C1:D15. Use the “Auto fit” option in the Format menu from the Home mode to make the report columns wide enough to accommodate the statistics' names. 3. In column B, put a label in B1 and some bin numbers (class boundaries) below it, as, for example, in the attached prototype. Use the Histogram tool in the Data Analysis toolkit to produce a histogram of the generated data in column based on the bin numbers in column B. Once done, click on the chart to activate it and use the mouse to "click, hold and roll” the chart to any place in the worksheet to better organize the report area. Also, “holding on" to the wholds" in the midpoints of the chart boundary, roll the sides to make the histogram the right size (bars clearly visible, text easily readable). Customize titles of the chart and the value axis (as, for example, in the prototype). 4. To facilitate Outliers Analysis, compute and report on the worksheet (under the Descriptive Statistics report) upper and lower fences for outliers by: a. The rule using the sample mean + 2s and sample mean – 2s (s: sample standard deviation); b. The 1.5*IQR rule using and reporting on the worksheet) 1st and 3d quartiles Q1 and Q3 of the sample data, the IQR (inter-quartile range) as Q3 - Q1, the lower fence by Q1 - 1.5*IQR and upper fence by Q3 +1.5*IQR; c. The 5th and 95th percentiles. 5. Open a text box (using the Text Box “A” button from the Insert mode) and put there a verbal report with your name, section number, “Lab 1" at the top and the seed number used to generate the data (should be the last 4 digits of your student ID). 6. In the Report, comment on how the shape of the histogram of the sample data agrees with the Normal distribution used to generate the data and whether the histogram indicates any outliers. Report how many Standard Errors fit into the difference between the Sample and the Population Means and how close is the ratio of the Sample Variance to the Population Variance = the square of 5 = 25) is to 1. Report the outliers determined by each rule.

92% Ili + BA m324lab1prototype T Chart Text Shape Media Comment Table View Zoom Add Category Insert Table + Sheet1 Sheet2 Sheet3 a A B с D E F G H 1 J K 11 18.9545545986 0.82881294899 19.2552432104 bin ## 10 15 20 25 30 More Frequency 1 5 12 10 2 0 X 10 15 Mean 20 Standard Error 25 Median 30 Mode Standard Deviation Sample Variance sample Kurtosis Skewness Р new Range Minimum Maximum WIA" Sum sum Count Histogram of the sample data 4.53959548114 20.6079271324 courses 0.93972062461 0240 -0.1403346697 21.4859483094 7.58121364517 29.0671619545 568.636637959 30 12 9 1 х bin ## 2 7.58121364 3 10.8707656 4 13.5811842 5 14.163761 6 14.218592 . 7 14.2275394 ce 8 15.854034 ad 9 16.3959224 1.We 10 16.894712: 11 17.8474818 12 18.134865. home 13 18.158216: ein 14 18.5961945 divent 15 18.811000! 16 To 18.9353739 10.*** 17 19.5751124 " 12 18 To o 19.901888: 19 19.9948386 20 20.2044998 21 20.2646174 O CASA re 22 20.415673 23 21.3795556 24 21.5103631 oran 25 22.0618074 26 22.227034: 27 22.6744146 34คราง 28 23.3402210 COCO 29 23.4670506 30 28.2815404 31 29.0671616 Frequency 6 Ji. 3 Q1 = Q3 = IQR = Lower fence = Upper fence = 5th percentile 95th percentile 16.5206200158 21.4776617263 4.95704171044 9.08505745021 28.9132242919 2700 12.0904537020 26.1150200281 0 10 15 30 More 20 25 generated data sample mean - 2s sample mean + 2s + 9.87536363634 28.0337455609 Name, section #, lab 1, seed # The histogram looks Normal, no apparent outliers on the chart. The difference between the Sample Mean 18.95 and the Population Mean mu = 20 is about 1.2 Standard Errors. The ratio of the Sample Variance 20.6 to the population variance 25 is about 0.82. By the x-bar +/-2s rule there is one low outlier 7.58 and one upper outlier 29.07. By the 1.5*IQR rule, there is one lower outlier 7.58 and one upper outlier 29.07. By the 5th and 95th percentile method, there is one more lower outlier 10.87 and one more upper outlier 28.28.