##################>### ############################################# #################### R#o#o#t# #E#n#t#r#y############################################################## ########################### ######W#o#r#k#s#h#e#e#t#D#a#t#a#1#7################################### ##################################################W#o#r#k#s#h#e#e#t #I#n#f#o#################################################################### ################### ##########C#o#m#p#r#e#s#s#e#d################################################# ################################################################# ########## ### ####### ####################################################################### ###! ###"#######$### %### ###############WORK########### ####D###C#:#\\#U#s#e#r#s#\\#a#n#d#r#e#\\#D#r#o#p#b#o#x#\\#S#u#m#m#e#r#2#0#1#6#\\#D#a# t#a#s#e#t#s#\\#C#O#N#T#E#N#T#d#a#t#a#s#e#t#s#\\#H#O#M#E#S#.#m#t#w#*########### ###H#O#M#E#S#.#m#t#w#(#W#2#)################################################### ########################### ### ######### @ ###############S#e#g#o#e# #U#I#2###############S#e#g#o#e# #U#I# #S#e#m#i#b#o#l#d#################*######ADDR ### ###CmColumnVar#ADDR8 ### ###CmColumnVar#########################[######################ADDR<0#'#######C mModelVariableNode#ADDR:_#&### ###CmModelNode############ADDR#"#######CmModelObject######################### \\###########################@########################################### #####################################_########################################## ################ ############;###d##O_Ykuvt#S# ## xvj $^#`N39Z1a! ##+##$## ; w##2zp##(1A)UF& R##9 {W)>S{##+O8\\F~mna#(~ SKS'#V rDu9jz#s;_ej jI'6T2#*Bic ?g#8g]pY#H# \\0)&-#t?c2vb#+%B-I? u{-CA`':## U9#=dr#63qYq ` ? ?v*N/)qC^\\##R#7#6tWYEj*N63q7#vxWGx W44:##d#PQ*2#4?#I'l#4$##w##X< O}UW]#m xw#R+BWqWS#mEMuYks+Xi#tu5z5t;+?3? (G#solg##A.~'#t[;_+#p0kO^wIu Ve;jL;,khS#^&q&\\^$V? o#-qlXxJh#'# eJY$U)ELfs##,`?'wa'?hnG?~N|0BhoFAkn#@?<$`7ax4l2]Ae#ovP##c B#s8Y#y ?.hu#3oS:s#m+H7+ %_&n)u#|/#aS7[{H#Vc@0#mH/#1(#K#{,##| s#b#hwdm##Wg##Qxa#(v##Ba)O&9p###################### ################################################################################ ################################## Math 2209 Minitab Assignment on Chapter 24 Summer Distance 2016 Is a house listing price a good predictor of its actual selling price? A random sample of 39 houses in Duchess County, New York was taken, and the listing price and actual selling price were recorded. In this assignment, you will look at the relationship between the LIST price (x) and SELL price (y) using regression analysis. If necessary, refer to the Minitab Survival Guide on your Lab Moodle site for more help. Use either Minitab version 17 or Minitab Express (not both!). MINITAB 17 INSTRUCTIONS 1. Open the Minitab data file HOMES.MTW from your class Moodle site. You will see the data file has 2 columns: C1 is LIST the listed sale price of the home (x, the explanatory variable, in hundred thousand dollars), C2 is SELL, which is actual sale price of the home (y, the response variable, in hundred thousand dollars). 2. Type your name and id number into the session window. 3. Make a scatterplot of SELL price vs. LIST price, with the regression line plotted on it. To do this, click on Stat > Regression > Fitted Line Plot.... SELL is the response variable and LIST is the explanatory or predictor variable. Click OK. Print out a copy of this graph. The regression output will appear in your session window. 4. Perform the regression of SELL on LIST and plot the residuals: Stat > Regression > Regression > Fit Regression Model..., Choose SELL as the response, LIST as the Continuous Predictor. Choose Graphs .. and select Four in one then hit OK and OK Print out a copy of this graph. The regression output will appear in your session window. 5. Find the predicted SELL when the LIST price is 3.500 ($350,000): Stat > Regression > Regression > Predict ... Leave choice as Enter Individual Values and type 3.500 into the first box under LIST. Hit OK. The results will appear in the session window. 6. Print out a copy of the Session window. Once you have completed the above steps, use the output to answer the questions on page 3. Make sure to submit: the page with your answers, the plots, and the session window. There will be points for good presentation, and points taken away for poor presentation (which means write clearly and staple your pages!). MINITAB EXPRESS INSTRUCTIONS ARE ON THE NEXT PAGE. MINITAB EXPRESS INSTRUCTIONS 1. Open the Minitab data file HOMES.MTW from your class Moodle site. You will see the data file has 2 columns: C1 is LIST the listed sale price of the home (x, the explanatory variable, in hundred thousand dollars), C2 is SELL, which is actual sale price of the home (y, the response variable, in hundred thousand dollars). 2. Perform the regression of SELL price on LIST price, make the scatterplot of SELL price vs LIST price with the regression line plotted on it and plot the residuals: Statistics > Simple Regression, Choose SELL as the Response (Y), LIST as the Predictor (X). Choose Graphs .. and select Residual plots then hit OK and OK The graphs and regression output appear in the Output window. Print all of this output by choosing File > Print. 3. Find the predicted SELL price when the LIST price is 3.500 ($350,000): Statistics > Predict ... Type 3.500 into the first box under LIST. Hit OK. The result will appear in the Output window. Choose File > Print to print this output. 4. In the Minitab spreadsheet, go to Column C3 and row 1. This means you'll be right beside the 4.000 which is the first value in the SELL column C2. Type Math2209 in this spot. Express on PC: Next choose Data > To Text. Enter C3 in the box Recode values in the following columns. Express on Mac: Choose Data > Recode > To Text. Then enter C3 in the box values in the following columns. Under the box Recoded value replace Math2209 with your name and student number. Then hit OK. The output will appear in the Output window. Choose File > Print to print this output. Make sure to submit: the page with your answers and all your output (plots and regression output). There will be points for good presentation, and points taken away for poor presentation (which means write clearly and staple your pages!). Math 2209 Answer Template for Minitab Assignment on Ch. 24 Name a) (16 pts) Check that the assumptions (and conditions) have been met, with reference to the appropriate plots where relevant. b) ( 1 pt) State the regression equation: c) Perform a test to assess whether the selling price increases with the listing price: (i) (1 pt) Ho: Ha: (ii) (2 pts) State the test statistic and p-value from the output: (remember to modify the P-value if needed). Test Statistic: (you don't need to define the parameter) P-value: (iii) (1 pt) Briefly assess the strength of the evidence. (iv) (5 pts) Give your conclusion in the context of the problem. Parts d) and e) on following page d) ( 5 pts) On the output, find the 95% CI for the mean selling price for a subpopulation and report it here: CI: Give an interpretation of this 95% CI in full context: e) (6 pts) On the output, find the 95% PI for the selling price and report it here: PI: Give an interpretation of this 95% PI in full context: Math 2209 Distance Minitab Assignment: Ch 25 One-Way ANOVA Summer 2016 A researcher is interested to see if there is a difference in the time spent waiting in traffic for various cities in the US. Three random samples of drivers are taken from different cities (Dallas, Boston and Detroit), and the time (in minutes) that they are stuck in traffic is recorded. Is there evidence that the three cities differ in traffic wait time? This data does not require a significance level, however for the sake of practice and interpretation, we will use a significance level of = 1%. Dallas 59 62 58 63 61 Boston 54 52 55 58 53 Detroit 53 56 54 49 52 Minitab Version 17 Instructions 1. Open the Minitab data file TRAFFIC.MTW from your class Moodle site. 2. Type your name and id number into the session window. 3. Use Minitab to conduct a one-way ANOVA and residual plots, and to obtain the Tukey pairwise intervals as follows: Stat - ANOVA - One-way and specify Response data are in a separate column for each factor level. Under Responses, add C1 Dallas, C2 Boston and C3 Detroit. Under Graphs select 3 in one then click OK. Under Comparisons click Tukey then click OK and OK. The ANOVA output appears in the session window. Print out a copy of the Residual plots and Tukey's Simultaneous 95% CIs. 5. Print out a copy of the session window. Once you have completed the above steps, use the output to answer the questions on the final page of this assignment. Make sure to submit: the page with your answers, the plots, and the session window. There will be points for good presentation, and points taken away for poor presentation (which means write clearly and staple your pages!). MINITAB EXPRESS INSTRUCTIONS ARE ON THE NEXT PAGE. *Source: Texas Transportation Institute. From: Introductory Statistics, Blumenhal. Minitab Express Instructions 1. Open the Minitab data file TRAFFIC.MTW from your class Moodle site. 2. Use Minitab to conduct a one-way ANOVA and residual plots, and to obtain the Tukey pairwise intervals as follows: select Statistics - ANOVA - One-Way ANOVA and specify Responses are in a separate column for each factor level. Under Responses add C1 Dallas, C2 Boston and C3 Detroit. Under Comparisons click Tukey (family error rate) and under Graphs click Residual plots. Click OK. The ANOVA output and graph appears in the output window. Print out the output using File - Print. 3.In the Minitab spreadsheet, go to Column C4 and row 1. This means you'll be right beside the 53 which is the first value in the Detroit column C3. Type Math2209 in this spot. Express on PC: Next choose Data > To Text. Enter C4 in the box Recode values in the following columns. Express on Mac: Choose Data > Recode > To Text. Then enter C4 in the box values in the following columns. Under the box Recoded value replace Math2209 with your name and student number. Then hit OK. The output will appear in the Output window. Choose File > Print to print this output. Once you have completed these steps, use the output to answer the questions on page 3. Make sure to submit: the page with your answers and all your output (plots and regression output). There will be points for good presentation, and points taken away for poor presentation (which means write clearly and staple your pages!). Math 2209 Answer Template for Minitab Assignment on Ch. 26 Name (a) (7 pts) Check the assumptions and state the conditions, using the residual plots where relevant. Independence: Equal Variance: Normality: (b) Is there evidence that that there is a difference in mean traffic wait time among the three cities? (2 pts) State the hypotheses in symbols: (1 pt) Test Statistic: ( 3pts ) Brief conclusion: Significance: Strength: (1 pt) P-value: (4 pts) Conclusion (in context): (c) (2 pts) Do the individual Tukey comparisons support the conclusion above? 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