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Use the "Job_prof.sta" file in the Statistica "Dat...

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Use the "Job_prof.sta" file in the Statistica "Datasets" file to answer these questions. In this example, a company gives its applicants 4 tests to predict their future job performance. 1) Run a multiple regression on this data using the results of the 4 examinations to predict job proficiency. Give each of the summary statistics for the overall model and explain what they tell you about this particular model. 2) Determine which variables are the most important and list the order from most important -1- to least important -4-. 3) Determine if a better model exists and give statistical evidence supporting your conclusion. 4) Write the mathematical equation for the best model.,i dont really know i am so confused with my professor,i believe is using statistica on graphing calculator or computer,There are 2 ways of performing Chi-square tests on STATISTICA. The method depends on the form of the data. One may have the original spreadsheet file, in which each individual?s ?score? on the category appears or one may be provided with the frequency of people in each category. I will deal with each of these separately. Chi-square Goodness-of-Fit Test Spreadsheet In this situation one has the full set of data, such as that provided for your assignment, SETA.STA. Suppose one wished to establish whether the proportions of the different population groups in the sample matched that of the population of the two provinces. The first thing one would have to do is to establish the frequency of each population group using the ?Frequency Tables? option in the Basic Statistics module. These frequencies are the observed frequencies. The expected frequencies would be derived from the census data and transformed to match the size of our sample. Thereafter you would perform the analysis in the way described in the following section. Frequencies provided For the Goodness-of-Fit test, one has to have both the raw frequencies and the expected frequencies. These must be captured into a file, such that the observed and expected frequencies are in two columns. The name of the category should be placed in the first column. To exemplify this, I will use Example 2.1.1 about the popularity of take-way food given in the notes on Revision of Basic Statistics. The data file would look like this: Chi-square on STATISTICA 2 of 4 Once this file has been saved, to analyse the data do the following: ? Switch to the STATISTICA module called ?Nonparametric Statistics?. ? From the menu, select the option ?Observed versus expected X2?. Click on OK. ? In the next screen, indicate the names of the variables containing the observed and expected frequencies. These then appear under the variable list, next to ?with observed:? and ?with expected:?. Click on OK. ? The next screen gives the results: Observed vs. Expected Frequencies (chi1) Chi-Square = 98.21875 df = 5 p < 0.000000 observed expected O - E (O-E)**2 C: 1 43.0000 64.0000 -21.0000 6.89063 C: 2 122.0000 64.0000 58.0000 52.56250 C: 3 84.0000 64.0000 20.0000 6.25000 C: 4 70.0000 64.0000 6.0000 0.56250 C: 5 38.0000 64.0000 -26.0000 10.56250 C: 6 27.0000 64.0000 -37.0000 21.39063 Sum 384.0000 384.0000 0.0000 98.21875 I have highlighted the line giving the overall Chi-square results. Chi-square Test of Contingency Spreadsheet When one has all the raw data, such as in data file SETA.STA, then one would calculate the Chi-square using the ?Tables and banners? programme in the ?Basic Statistics? module. ? On the first screen, under either the ?Crosstabulations? or the ?Stub-and banner table? heading click on ?Specify tables?. ? If you did this under ?Crosstabulations? heading then 6 columns will appear, listing all the variables of the file (usethe first 2). If you did this under the ?Stuband banner? heading, then only 2 columns will appear. Click on the first desired variable in the first column, and the second desired variable in the second column. Thus, in the question in Assignment 1, question 2c, you would click on SEX in one column and LANG2 in the other, where LANG2 is a variable in which you have recoded every language except 1 (English) and 2 (Afrikaans) as missing. Click on OK. ? You are taken back to the original screen. At this point, if you need to specify that the analysis should be done for only a certain segment of the sample (e.g. on Coloured adolescents only) click on the ?Select Cases? button under the OK button. If you do not need to select particular cases, skip the next step. ? The Case Selection Conditions screen then opens. Click on the box next to the ?Enable Selection Condition?. Next to the ?Include? section click on ?Specific, selected by?. The ?By Expression? and ?By case number? boxes then become active. In the ?By Expression? box, type your condition ? in this example, PGP=3. In SETA, the variable PGP held the population group of the adolescents, and the code for Coloured was 3. Click on OK. This will take you back to the ?Crosstabulations Tables? screen again. Click on OK. Chi-square on STATISTICA 3 of 4 ? You are taken back to the Options page of the ?Crosstabulations Tables Results? screen. Here you can chose whether (among other options) you want the tables to provide the frequencies as percentages, and can select what kind of statistics one want. In this case we select the ?Pearson and M-L chi-square?. ? Select the Advanced page and click on ?Detailed two-way tables?. Two screens of results appear, one giving all the cell numbers and percentage, and the other giving the chi-square results. Frequencies provided Sometimes we are presented with the results of a crosstabulation, without the raw data. Thus we would be given the frequency in each cell, as in Example 2.3.1 in the notes on Revision of Basic Statistics. Recall, the frequencies given were: Chinese Pizza Chicken Hamburger Pasta Fish Southern Suburb 43 122 84 70 38 27 Northern Suburb 10 150 60 230 30 20 Atlantic Seaboard 45 60 58 30 74 110 This analysis is little more complex. You have to do the following: ? Create a file in which you have one variable (SUBURB) holding the suburb, another (TYPE) holding the type of take-away, and a third (FREQ) holding the frequency of the relevant cell. It would look like this: ? Switch to ?Tables and banners? programme in the ?Basic Statistics? module. ? On the first screen, under either the ?Multiway crosstabulation tables? or the ?Stub-and banner table? heading click on ?Specify tables?. ? The screen with the columns of variables will appear. Chi-square on STATISTICA 4 of 4 ? For the first variable in the first column click on SUBURB, for the second click on TYPE. Click on OK. ? You are taken back to the ?Crosstabulation Tables? screen. Click the button next to ?Use selected grouping codes only?, then on Codes. The ?Select codes for grouping factors? screen will appear. Select ?All? for each variable. Check that the codes that appear are accurate. Click on OK. ? Back in the ?Crosstabulation Tables? screen, click on the button next to the Select Cases button labelled W. A screen called ?Analysis/Graph Case Weights? screen appears. Click the buttons next to: - Use weights for this analysis only - Status: On In the box labelled ?Weight Variable? type the name of the variable holding the frequencies of the cells. In the above example this was called FREQ. Click on OK. ? You will be taken back to the ?Crosstabulation tables? screen. Once again, click on OK. ? The screen called Crosstabulation Results will appear. Select the Option page. In the section headed, Compute Tables, click on Percentages of total counts, Percentages of row counts, and/or Percentages of column counts. ? Under the section headed, Statistics for 2-way Tables, click on Pearson & M-L Chi-square ? Select the Advanced page and click the button labelled ?Detailed 2-way tables?. Two screens of results appear, one giving all the cell numbers and percentage, and the other giving the chi-square results: 2-Way Summary Table: Observed Frequencies (chi3) Marked cells have counts > 10 TYPE TYPE TYPE TYPE TYPE TYPE Row South 43 122 84 70 38 27 384 Column % 43.88% 36.75% 41.58% 21.21% 26.76% 17.20% Row % 11.20% 31.77% 21.88% 18.23% 9.90% 7.03% North 10 150 60 230 30 20 500 Column % 10.20% 45.18% 29.70% 69.70% 21.13% 12.74% Row % 2.00% 30.00% 12.00% 46.00% 6.00% 4.00% Atlantic 45 60 58 30 74 110 377 Column % 45.92% 18.07% 28.71% 9.09% 52.11% 70.06% Row % 11.94% 15.92% 15.38% 7.96% 19.63% 29.18% Totals 98 332 202 330 142 157 1261 Statistics: SUBURB(3) x TYPE(6) (chi3) Chi-square df p Pearson Chi-square 362.4026 df=10 p=0.0000 M-L Chi-square 363.6237 df=10 p=0.0000 Gillian Finchilescu Revised January 2003,okay thanks,i didn't get any answers from you is their something wrong

 

Paper#13567 | Written in 18-Jul-2015

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