ISSCM 491 Survey Data Using PhStat

Anonymous
timer Asked: Feb 3rd, 2019
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Question Description

My Occupational group is Group # 3 Admin Support

Please Use The Program PhStat!!!

there will be a file you can download ( PHStat.xlam PHStat.xlam )if you do not have PhStat, and also another file to show you how to use PhStat to get the answers. ( Ph Stat Handout 2016.pdf )

- everything else should be pretty straight forward in the excel spreadsheet it shows and tells you exactly what to do. - the file for this assignment is hw8.xlsx. Do not do more than is asked please.

Also the Survey Data ISSCM491.xlsx file is attached as (if needed) to look at this for this homework assignment (if it is) mentioned inside hw8.xlsx

Townes.HW1.xlsx will show you the The Correct way on how to answer and submit this assignment, so you could take a look at that to help how to format to answer the questions.

Unformatted Attachment Preview

(Note that this homework has 2 questions) Homework 8 Question 1. The Child Health and Development collected data to study the relationship between the infant weights and several related variables. A sample of 40 observations are given in the next worksheet Model Building - Stepwise Regression (Backward Elimination Method): (a) Follows the process steps described below to obtain a significant regression model. Step 1. Determine the estimated regression equation containing ALL six independent variables (gestation, height, smoke, age, weight, and parity). (a) What is the estimated regression equation with these six independent variables? (b) What percent of the total variation in baby weights is explained by this regression? (c) Which independent variables are NOT significant in this model at a = 0.05? Step 2. Eliminate non-significant variables, one at a time, until you obtain a regression model with all independent variables significant. Process steps: Step 2a. Remove the non-significant independent variable(s), one at a time, starting with the variable that has the highest p-value first. Obtain the new regression model. Step 2b. Check the independent variables for significance at a=0.05.. (i) If all independent variables are significant, stop. This is the significant model. (ii) If some independent variables in the model are not significant, repeat Step 2. (b) These process steps described above will result in a regression model that has significant independent variables only. What is the significant model found after completing the process steps above? (c) Now use the stepwise regression available in PHStat. Run stepwise regression (using backward elimination approach) to obtain a significant regression model. What is the model found by the PHStat stepwise regression? (d) Compare the significant regression models obtained in (b) and (c). Are they different? If they are different, which model is performing better? (e) Using the model you found to be performing better in (d), construct 95 percent confidence interval estimate for the mean baby weights for the newborns with the following values: (i) (i) Baby 1 Baby 2 gestation height smoke age weight parity 283 64 1 22 140 0 gestation height smoke age weight parity 283 64 1 25 160 1 (f) Which confidence interval estimate has a larger interval? Why? When you run stepwise regression in question (c), if you get an error message like the following, that means the PHStat failed. Please copy the data into a NEW FILe and run the stepwise regression from this new file. bwt gestation height smoke age weight parity Variables 128 125 114 130 116 81 124 125 110 125 138 142 115 102 140 133 127 104 119 152 123 143 131 141 129 113 119 109 104 131 110 148 137 117 115 98 136 121 132 93 290 286 290 285 248 256 287 292 262 279 294 284 278 280 294 276 290 274 275 301 284 273 308 319 277 282 292 295 280 282 293 279 283 283 302 250 303 276 285 264 64 64 66 63 66 60 62 65 66 63 64 66 60 55 61 63 66 62 67 65 65 66 65 67 66 59 62 63 68 66 64 71 65 63 67 56 68 71 63 60 0 0 0 1 0 1 1 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 1 1 1 0 1 0 0 0 0 1 0 1 0 1 22 21 30 23 28 30 27 22 25 23 40 39 23 38 25 22 35 20 42 29 20 19 40 20 30 36 33 23 27 21 28 27 20 27 22 35 20 23 25 36 118 139 160 128 135 148 105 122 140 104 125 132 102 140 103 119 165 115 156 150 120 135 160 140 142 140 118 103 146 126 135 189 157 108 135 122 148 152 140 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 1 0 1 0 1 1 1 1 0 1 0 1 0 1 1 1 0 Bwt Gestation Height Smoke Age Weight 122.50 15.428911 283.3 64.1 0.4 27.4 0.4 Parity When you run stepwise reg if you get an error message means the PHStat failed. Pl NEW FILe and run the stepwis file. Bwt 180 160 140 120 100 4900.00000 4900.0000 Baby weights in ounces Length of pregnancy in days Mother's height in inches =1 if mother is smoker. = 0 Nonsmoker Mother's age in years Mother's pregnancy weight = 0 if the baby is first born =1 otherwise Bins 90 100 110 120 130 140 150 160 170 180 en you run stepwise regression in question (c), ou get an error message like the following, that ans the PHStat failed. Please copy the data into a W FILe and run the stepwise regression from this new Bwt vs Gestation Bwt 180 160 140 120 100 80 60 40 240 260 280 Gestation 300 320 340 Mid 95 105 115 125 135 145 155 165 175 180 4900.0 4900. bwt gestation height Weight smoke age 128 125 114 130 116 81 124 125 110 125 138 142 115 102 140 133 127 104 119 152 123 143 131 141 129 113 119 109 104 131 110 148 137 117 115 98 136 121 132 93 290 286 290 285 248 256 287 292 262 279 294 284 278 280 294 276 290 274 275 301 284 273 308 319 277 282 292 295 280 282 293 279 283 283 302 250 303 276 285 264 64 64 66 63 66 60 62 65 66 63 64 66 60 55 61 63 66 62 67 65 65 66 65 67 66 59 62 63 68 66 64 71 65 63 67 56 68 71 63 60 118 139 160 128 135 148 105 122 140 104 125 132 102 140 103 119 165 115 156 150 120 135 160 140 142 140 118 103 146 126 135 189 157 108 135 122 148 152 140 100 0 0 0 1 0 1 1 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 1 1 1 0 1 0 0 0 0 1 0 1 0 1 22 21 30 23 28 30 27 22 25 23 40 39 23 38 25 22 35 20 42 29 20 19 40 20 30 36 33 23 27 21 28 27 20 27 22 35 20 23 25 36 122.50 15.428911 283.3 64.1 0.4 27.4 4900.00000 4900.0000 Question 2 (Y) ($1,000) MARKET REAL ESTATE DATA (X1) (X2) (X3) (Sq Feet) AREA ($1,000) (X4) (Yes/No) (Number) ASSESS BASEM BEDRM 128 No 1 101 No 1 (Area) LOCAT Loc 2 Loc 2 1 2 139 172 952 1255 3 4 5 178 180 183 1663 1575 1209 88 110 125 No No No 1 1 1 Loc 1 Loc 3 Loc 3 6 7 8 9 10 183 198 205 205 216 1689 1330 1437 1598 1377 81 147 143 103 123 No Yes Yes Yes Yes 1 1 1 1 1 Loc 1 Loc 1 Loc 2 Loc 1 Loc 1 1607 1225 1610 1157 1262 1426 1185 1580 1215 1444 1808 1925 1577 1010 1365 1342 1460 1573 1425 1629 1589 1855 1769 1768 1505 1824 1888 1607 1848 1792 1881 1936 1832 1873 1769 1691 1855 1824 1957 963 1178 1992 1545 1846 1593 2030 1920 1904 2170 2049 1159 1558 1467 2034 2004 2270 2275 2186 2319 2272 115,707 1,652.96 332.024 123 99 114 132 119 79 125 103 125 156 139 165 139 123 125 123 99 128 121 136 128 132 143 156 103 145 134 178 143 150 167 174 165 125 154 167 183 185 174 125 130 141 143 152 136 176 178 167 165 178 108 112 141 163 169 125 128 165 176 130 9,636 137.66 25.997 Yes No No No No Yes Yes Yes Yes Yes Yes Yes Yes No No No No Yes Yes Yes Yes Yes Yes No Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes No No Yes Yes No Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes 1 2 2 2 2 2 2 2 2 2 2 2 2 3 3 2 3 3 2 3 3 2 3 3 2 3 3 2 3 3 2 3 3 2 3 3 2 3 3 3 4 4 3 4 4 3 4 4 3 4 4 4 4 4 4 4 4 4 4 4 185 2.64 Loc 1 Loc 3 Loc 1 Loc 1 Loc 1 Loc 1 Loc 1 Loc 3 Loc 2 Loc 3 Loc 1 Loc 1 Loc 1 Loc 2 Loc 2 Loc 1 Loc 2 Loc 3 Loc 2 Loc 3 Loc 3 Loc 3 Loc 3 Loc 1 Loc 2 Loc 3 Loc 3 Loc 3 Loc 3 Loc 3 Loc 1 Loc 1 Loc 3 Loc 2 Loc 2 Loc 2 Loc 3 Loc 2 Loc 3 Loc 2 Loc 2 Loc 3 Loc 1 Loc 2 Loc 2 Loc 1 Loc 2 Loc 3 Loc 1 Loc 2 Loc 1 Loc 3 Loc 1 Loc 1 Loc 3 Loc 3 Loc 2 Loc 1 Loc 3 Loc 3 11 224 12 161 13 172 14 172 15 180 16 189 17 200 18 200 19 200 20 209 21 222 22 229 23 229 24 145 25 172 26 185 27 191 28 207 29 207 30 209 31 209 32 211 33 211 34 213 35 213 36 222 37 224 38 227 39 231 40 231 41 235 42 235 43 240 44 240 45 240 46 242 47 246 48 268 49 268 50 167 51 189 52 216 53 218 54 222 55 224 56 238 57 246 58 249 59 257 60 266 61 191 62 194 63 196 64 231 65 255 66 255 67 260 68 262 69 266 70 275 SUM 15045 AVG 214.929 St Dev 31.193 Dummy Variables: X3 = Basement X3 = 1 Finished Basement (Yes) X3 = 0 No Finished Basement (No) X5 = Location (Location 2 or not) X5 = 1 if in location 2 X5 = 0 if not in location 2 X6 = Location (Location 3 or not) X6 = 1 if in location 3 X6 = 0 if not in location 3 CORRELATION COEFFICIENTS MARKET AREA ASSESS BASEM BEDRM MARKET AREA ASSESS BASEM BEDRM LOCAT 1 0.8617 1 0.6924 0.5266 1 0.7016 0.5180 0.4405 1 0.4897 0.4787 0.4197 0.2761 1.0000 Question: To answer the regression questions below, you first need to enter numeric values (0 or 1) for BASEMENT column (X3), and 0 or 1 for LOCATION variables (X5 and X6) as defined above. (a) Use stepwise regression to determine the best Regression Model to predict the market value of the houses. Which variables are included in the model determined by stepwise regression? (b) What is the estimated regression equation with these variables? (c) What percentage of the total variations in market VALUEs is explained by this regression model? (d1) Using this regression model, calculate the predicted market VALUE for a house in location 1 with a BASEMENT, AREA=1652 and ASSESS=137. (d2) Using this regression model, calculate the predicted market VALUE for a house in location 2 with a BASEMENT, AREA=1652 and ASSESS=137. (d3) Using this regression model, calculate the predicted market VALUE for a house in location 3 with a BASEMENT, AREA=1652 and ASSESS=137. Page 4 of 4 (Homework 8) Max Min 275 139 Dr. Guder USING EXCEL and PHStat Page 0 MS Excel "Add-Inns": Data Analysis MS Excel "Add-Ins": PHStat Lecture "Add-in" to Use How Class 1 Data Analysis Data --> Data Analysis --> Histogram….. 2 Class 1 PHStat PHStat --> Descriptive Statistics--> Histogram and Polygons 2a Descriptive Statistics (Excel) Descriptive Statistics (PHStat) Class 1 Data Analysis Data --> Data Analysis --> Descriptive Statistics….. 3 150 Class 2 Data Analysis PHStat --> Descriptive Statistics--> Descriptive Summary… 4 150 Normal Distribution (probability Calculation) Class 2 PHStat PHStat --> Probability and Prob Distributions --> Normal … 5, 6 246 Mean (sigma known) Class 2 PHStat PHStat --> Confidence Interval --> Estimate for the Mean, sigma known … 7 302 Mean (sigma unknown) Class 2 PHStat PHStat --> Confidence Interval --> Estimate for the Mean, sigma unknown … 7 302 Proportion Class 2 PHStat PHStat --> Confidence Interval --> Estimate for the Proportion … 8 303 Mean Class 2 PHStat PHStat --> Sample Size --> Determination for the Mean 8 303 Proportion Class 2 PHStat PHStat --> Sample Size --> Determination for the Proportion 8 303 Mean (sigma known) Class 3 PHStat PHStat --> One-Sample Tests --> Z Test for the Mean, sigma known … 9 339 Mean (sigma unknown) Class 3 PHStat PHStat --> One-Sample Tests --> t Test for the Mean, sigma unknown … 10 339 Proportion Class 3 PHStat PHStat --> One-Sample Tests --> Z Test for the Proportion … 10 341 Subject/Task Data - Service Employees Histogram, Frequency Distribution Handout Textbook Page(s) Page(s) 1 95 Interval Estimation for Sample Size for Estimating Hypothesis Testing for One-Sample Tests Two-Sample Tests Differences in Two Means (independent populat Class 3 PHStat PHStat ---> Two-Sample Tests (Summarized Data) --> Pooled Variance t Test for… 11 382 Differences in Two Means (related populations) Class 3 PHStat PHStat --> Two-Sample Tests (Unsummarized) --> Paired t Test … 12 384 Class 4 Data Analysis Analysis of Variance (ANOVA) One-Way ANOVA Tukey-Kramer Procedure Using PHStat Two-Way ANOVA (Excel) PHStat Class 4 Two-Way ANOVA (PHStat) Correlation Data Analysis PHStat Class 6 Data Analysis Data --> Data Analysis --> Anova: Single Factor 13 PHStat --> Multiple-Sample Tests --> One-Way ANOVA (Check Tukey-Kramer Proc) 15 424 Data --> Data Analysis --> Anova: Two Factor 14 426 PHStat --> Multiple-Sample Tests --> Two-Way ANOVA 16 Data --> Data Analysis --> Correlation … 17 Regression Analysis Simple Linear Regression Multiple Regression Class 6 PHStat PHStat --> Regression --> Simple Linear Regression … 18-19 520-523 Classes 7, 8 PHStat PHStat --> Regression --> Multiple Regression … 20-21 564-569 Class 9 Excel Drawing Line Chart (Graph) in Excel 2013 Line Chart Starting PHStat (MS Excel 2003, 2010, and 2013) Step 0. Save the PHStat file (PHStat.xlam) to your desktop. When saved, it will be a shortcut looking an Excel file with the title PHStat2 or PHStat Step 1. Double click on this shortcut. Choose Enable Macros option. This will add another pane called Add-in Step 2. Select Add-in to get to PHStat 22 SERVICE EMPLOYEES (Occup Group = 10) DATA (Explanations on pages 2-3 are based on the data shown below) Page 1 Page 2 USING EXCEL TO OBTAIN HISTOGRAM and FREQUENCY DISTRIBUTION Choose the following: Data ---> Data Analysis ---> Histogram ---> (Fill out the window as shown below) Output/Result: Frequency 0 1 9 12 4 2 1 Histogram Frequency Bins 15.0 25.0 35.0 45.0 55.0 65.0 75.0 15 10 5 0 15.0 25.0 35.0 45.0 55.0 65.0 75.0 Bins USING PHStat TO OBTAIN HISTOGRAM Page 2a Choose the following: PHStat  Descriptive Statistics  Histogram and Polygons You need to provide Cell ranges for three different data:  Variable Cell Range (The area/column that has the INCOME data – D5.D59 in my case. Note D5 has the title/Label)  Bins Cell Range (The area where you entered the interval points as 20, 30, 40, 50, 60, 70, 80, 90)  Midpoints Cell Range (The area where you entered midpoints of the bin values as 25, 35, 45, 55, 65, 75, 85) Suppose the data values are given in column D (D5.D59), where D5 has the column title/label. Bins (interval points) are entered in the range S2.S10 and Midpoints are entered in the range T2.T9 as shown below. You will then enter the ranges in the Histogram & Polygons windows as shown below: The result will be similar to the following: USING EXCEL TO OBTAIN DESCRIPTIVE STATISTICS Page 3 Choose the following: Data ---> Data Analysis ---> Descriptive Statistics... ---> (Fill out the window as shown below) Output/Result: INCOME (000) Mean 31.483 Standard Error 2.300 Median 29.4 Mode #N/A Standard Deviation 12.388 Sample Variance 153.451 Kurtosis -0.112 Skewness 0.855 Range 44 Minimum 15.3 Maximum 59.3 Sum 913 Count 29 Confidence Level (95%) 4.712 WORKed Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Sum Count Confidence Level(95%) EDUC 20.690 2.090 21 24 11.254 126.650 0.945 0.816 46 3 49 600 29 4.281 Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Sum Count Confidence Level (95% 12.862 0.343 13 14 1.846 3.409 -0.129 -0.478 7 9 16 373 29 0.702 USING PHSTAT TO OBTAIN DESCRIPTIVE STATISTICS (Single Group) PHStat ---> Descriptive Statistics --> Descriptive Summary --> (Select) Single Group Variable --> (Fill out the window as shown below) Descriptive Summary INCOME Mean 31.483 Median 29.4 Mode #N/A Minimum 15.3 Maximum 59.3 Range 44 Variance 153.451 Standard Deviation 12.388 Coeff. of Variation 39.35% Skewness 0.855 Kurtosis -0.112 Count 29 Standard Error 2.300 USING PHSTAT TO OBTAIN DESCRIPTIVE STATISTICS (Multiple Groups) PHStat ---> Descriptive Statistics --> Descriptive Summary --> (Select) Single Group Variable --> (Fill out the window as shown below) Descriptive Summary 0 (Female) Mean Median Mode Minimum Maximum Range Variance Standard Deviation Coeff. of Variation Skewness Kurtosis Count Standard Error 22.991 21.6 #N/A 15.3 33.7 18.4 38.809 6.230 27.10% 0.477 -1.096 11 1.878 1 (Male) 36.672 33.9 #N/A 21.1 59.3 38.2 154.739 12.439 33.92% 0.501 -0.938 18 2.932 Page 4 Page 5 USING PHStat TO OBTAIN PROBABILITY FOR NORMAL DISTRIBUTION Choose the following: PHStat ---> Probability & Prob Distributions ---> Normal… ---> (Fill out the window as shown below) Example 1(a) - Class 2 Example 1(b) - Class 2 Output/Result: Example 1(a) Example 1(b) Common Data Mean Standard Deviation Common Data 68 2 Probability for a Range From X Value 68 To X Value 70 Z Value for 68 0 Z Value for 70 1 P(X<=68) 0.5000 P(X<=70) 0.8413 P(68<=X<=70) 0.3413 Mean Standard Deviation 68 2 Probability for X <= X Value 71 Z Value 1.5 P(X<=71) 0.9332 Answer Answer Page 6 Example 1(c) - Class 2 Example 1(c) Common Data Mean Standard Deviation Probability for X > X Value Z Value P(X>71.3) 68 2 71.3 1.65 0.0495 Answer Example 3(a) - Class 2 Example 3(c) Common Data Mean 68 Standard Deviation 0.5 Probability for a Range From X Value 67.5 To X Value 69.0 Z Value for 67.5 Z Value for 69 P(X<=67.5) P(X<=69) P(67.5<=X<=69) -1 2 0.1587 0.9772 0.8186 Answer USING PHStat TO OBTAIN CONFIDENCE INTERVAL ESTIMATE (Population Standard Deviation () is Known) Page 7 Choose the following: PHStat ---> Confidence Intervals ---> Estimate for the Mean, Sigma known… ---> (Fill out the window as shown below Example 1(a) Data Population Standard Deviation Sample Mean Sample Size 2 67.5 100 Confidence Level 98% Intermediate Calculations Standard Error of the Mean Z Value Interval Half Width 0.2 -2.326 0.465 Confidence Interval Interval Lower Limit Interval Upper Limit 67.035 67.965 USING PHStat TO OBTAIN CONFIDENCE INTERVAL ESTIMATE (Population Standard Deviation () is Unknown) Choose the following: PHStat ---> Confidence Intervals ---> Estimate for the Mean, Sigma unknown… ---> (Fill out the window as shown below) Example 3 Data Sample Standard Deviation Sample Mean Sample Size Confidence Level Intermediate Calculations Standard Error of the Mean Degrees of Freedom t Value 144 266 9 90% 48 8 1.860 Interval Half Width 89.258 Confidence Interval Interval Lower Limit Interval Upper Limit 176.74 355.26 USING PHStat TO OBTAIN CONFIDENCE INTERVAL ESTIMATE for PROPORTION Page 8 Choose the following: PHStat ---> Confidence Intervals ---> Estimate for Proportion... ---> (Fill out the window as shown below) Data Sample Size Number of Successes Confidence Level 600 300 0.99 Intermediate Calculations Sample Proportion Z Value Standard Error of the Proportion Interval Half Width 0.5 -2.576 0.020 0.0526 Confidence Interval Interval Lower Limit Interval Upper Limit 0.447 0.553 USING PHStat TO DETERMINE the SAMPLE SIZE (n) for Mean PHStat ---> Sample Size ---> Determination for the Mean…---> (Fill out the window as shown below) Example 5 Data Population Standard Deviation Sampling Error Confidence Level 4 1 95% Intemediate Calculations Z Value Calculated Sample Size -1.960 61.463 Result Sample Size Needed 62 USING PHStat TO DETERMINE the SAMPLE SIZE (n) for Proportion PHStat ---> Sample Size ---> Determination for the Proportion…---> (Fill out the window as shown below) Example 8 - Class 2 Data Estimate of True Proportion Sampling Error Confidence Level 0.50 0.03 0.99 Intermediate Calculations Z Value Calculated Sample Size -2.58 1843.03 Result Sample Size Needed 1844 USING PHStat FOR HYPOTHESIS TESTING for MEAN Page 9 Choose the following: PHStat ---> One-Sample Tests ---> Z Test for the Mean, sigma known… ---> (Fill out the window as shown below) Example 1 - Class 3 Data = Null Hypothesis Level of Significance Population Standard Deviation Sample Size Sample Mean 64 0.05 0.5 25 63.85 Intermediate Calculations Standard Error of the Mean Z Test Statistic 0.1 -1.50 Two-Tail Test Lower Critical Value Upper Critical Value p-Value -1.960 1.960 0.1336 Do not reject the null hypothesis PHStat ---> One-Sample Tests ---> Z Test for the Mean, sigma known… ---> Example 2 - Class 3 Data Null Hypothesis = Level of Significance Population Standard Deviation Sample Size Sample Mean 700 0.05 75 225 704 Intermediate Calculations Standard Error of the Mean Z Test Statistic 5 0.80 Upper-Tail Test Upper Critical Value p-Value Do not reject the null hypothesis 1.645 0.212 PHStat ---> One-Sample Tests ---> t Test for the Mean, sigma unknown… ---> Page 10 (Fill out the window as shown below) Example 4 - Class 3 Data = Null Hypothesis Level of Significance Sample Size Sample Mean Sample Standard Deviation 700 0.10 64 710 48 Intermediate Calculations Standard Error of the Mean Degrees of Freedom t Test Statistic 6 63.00 1.6667 Upper-Tail Test Ca Lower Critical Value Upper Critical Value p-Value -1.6694 1.2951 0.0503 Reject ...
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Tutor Answer

uoscar
School: Carnegie Mellon University

The hw8.xlsx file contains the solution.The Analysis_hw8.xlsx file contains steps/analyses that were used in arriving at the result in hw8.xlsx.

(Note that this homework has 2 questions)

Homework 8

Question 1.
The Child Health and Development collected data to study the relationship between the infant weights
and several related variables.
A sample of 40 observations are given in the next worksheet
Model Building - Stepwise Regression (Backward Elimination Method):
(a) Follows the process steps described below to obtain a significant regression model.
Step 1. Determine the estimated regression equation containing ALL six independent variables
(gestation, height, smoke, age, weight, and parity).
(a) What is the estimated regression equation with these six independent variables?
(b) What percent of the total variation in baby weights is explained by this regression?
(c) Which independent variables are NOT significant in this model at a = 0.05?
Step 2. Eliminate non-significant variables, one at a time, until you obtain a regression model
with all independent variables significant.
Process steps:
Step 2a. Remove the non-significant independent variable(s), one at a time, starting
with the variable that has the highest p-value first. Obtain the new
regression model.
Step 2b. Check the independent variables for significance at a=0.05..
(i) If all independent variables are significant, stop. This is the significant model.
(ii) If some independent variables in the model are not significant, repeat Step 2.
(b) These process steps described above will result in a regression model that has significant
independent variables only. What is the significant model found after completing the process
steps above?
(c) Now use the stepwise regression available in PHStat. Run stepwise regression (using
backward elimination approach) to obtain a significant regression model.
What is the model found by the PHStat stepwise regression?
(d) Compare the significant regression models obtained in (b) and (c). Are they different? If
they are different, which model is performing better?
(e) Using the model you found to be performing better in (d), construct 95 percent confidence
interval estimate for the mean baby weights for the newborns with the following values:
(i)
(i)

Baby 1
Baby 2

gestation

height

smoke

age

weight

parity

283

64

1

22

140

0

gestation

height

smoke

age

weight

parity

283

64

1

25

160

1

(f) Which confidence interval estimate has a larger interval? Why?

When you run stepwise regression in question (c),
if you get an error message like the following, that
means the PHStat failed. Please copy the data into a
NEW FILe and run the stepwise regression from this new
file.

Question 2
(Y)
($1,000)
MARKET

REAL ESTATE DATA
(X1)
(X2)
(X3)
(Sq Feet)
AREA

($1,000)

(X4)

(Yes/No) (Number)

ASSESS BASEM BEDRM
128
No
1
101
No
1

(Area)

LOCAT
Loc 2
Loc 2

1
2

139
172

952
1255

3
4
5

178
180
183

1663
1575
1209

88
110
125

No
No
No

1
1
1

Loc 1
Loc 3
Loc 3

6
7
8
9
10

183
198
205
205
216

1689
1330
1437
1598
1377

81
147
143
103
123

No
Yes
Yes
Yes
Yes

1
1
1
1
1

Loc 1
Loc 1
Loc 2
Loc 1
Loc 1

1607
1225
1610
1157
1262
1426
1185
1580
1215
1444
1808
1925
1577
1010
1365
1342
1460
1573
1425
1629
1589
1855
1769
1768
1505
1824
1888
1607
1848
1792
1881
1936
1832
1873
1769
1691
1855
1824
1957
963
1178
1992
1545
1846
1593
2030
1920
1904
2170
2049
1159
1558
1467
2034
2004
2270
2275
2186
2319
2272
115,707
1,652.96
332.024

123
99
114
132
119
79
125
103
125
156
139
165
139
123
125
123
99
128
121
136
128
132
143
156
103
145
134
178
143
150
167
174
165
125
154
167
183
185
174
125
130
141
143
152
136
176
178
167
165
178
108
112
141
163
169
...

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