Create the Descriptive Statistics for "Price" and "SQFT" - Include a 95% Confidence Test on the Population Mean Produce a Scatterplot for Price (Dependent) and SQFT (Independent) (PLACE BELOW) Conduct a 95% Hypothesis Test (i.e., ? = .05) to determine if the Mean Price of Houses is greater than $242,512. Use a Population Standard Deviation of $172,000 for your Z-test (i.e., we are determing if the mean has increased from the previous year). Answers From the Scatterplot (placed below), is there a relationship between Price and SQFT (Yes or No)? What is the Mean of Price? What is the Lower Limit of the 95% Confidence Test? What is the Upper Limit of the 95% Confidence Test? This sample is from 2017. A previous years study showed the Mean Price to be $242,512. The City Mayor recently claimed that the local economic boom has now increased that Mean to over $250,000! Using the 95% confidence interval, is his claim justified? (Yes or No) Explain: What are your Hypotheses for the Test in Step C? (e.g., Ho: µ = value; H1: µ ? value) What is your decision rule? What is your decision regarding the Null Hypothesis?
Location | U/S/R | Lot (Acres) | Garage | BRs | Baths | Age | Sq. Ft. | Price |
10 | R | 2 | 0 | 2 | 1 | 27 | 1100 | 54000 |
2 | U | 0.25 | 1 | 3 | 2 | 26 | 1875 | 98000 |
5 | S | 0.25 | 0 | 2 | 1.5 | 82 | 1350 | 125700 |
6 | S | 0.5 | 2 | 3 | 2 | 11 | 2612 | 250000 |
9 | S | 0.5 | 1 | 3 | 2 | 17 | 2190 | 411500 |
1 | U | 0.25 | 0 | 3 | 1 | 21 | 1800 | 56500 |
3 | S | 0.25 | 2 | 3 | 2 | 6 | 1605 | 289500 |
7 | R | 12 | 2 | 3 | 2.5 | 72 | 2199 | 420000 |
4 | S | 0.4 | 2 | 3 | 2 | 15 | 2120 | 199800 |
10 | R | 1 | 1 | 2 | 2 | 12 | 1950 | 77000 |
2 | U | 0.5 | 0 | 2 | 2 | 16 | 1420 | 78600 |
5 | S | 0.75 | 2 | 3 | 2 | 22 | 2090 | 199800 |
6 | S | 0.5 | 2 | 3 | 2.5 | 9 | 2770 | 279500 |
9 | S | 1 | 3 | 5 | 5 | 4 | 3650 | 842000 |
1 | U | 0.25 | 1 | 3 | 1.5 | 28 | 1600 | 66720 |
3 | S | 0.5 | 2 | 3 | 2 | 11 | 2288 | 311450 |
7 | R | 1.5 | 2 | 3 | 2 | 21 | 2000 | 311520 |
4 | S | 0.25 | 1 | 3 | 2 | 9 | 1880 | 187500 |
10 | R | 3 | 1 | 4 | 2 | 35 | 3011 | 98000 |
2 | U | 0.4 | 2 | 3 | 2 | 4 | 2980 | 112000 |
5 | S | 0.25 | 0 | 3 | 2 | 11 | 1850 | 146850 |
6 | S | 0.5 | 3 | 4 | 2.5 | 3 | 3520 | 301500 |
9 | S | 0.75 | 3 | 4 | 3.5 | 9 | 3300 | 690000 |
1 | U | 0.5 | 1 | 3 | 1.5 | 37 | 1905 | 71200 |
3 | S | 0.25 | 2 | 3 | 2 | 5 | 2850 | 275000 |
7 | R | 10 | 3 | 4 | 2 | 2 | 3250 | 598230 |
4 | S | 0.4 | 2 | 3 | 2 | 3 | 1900 | 176500 |
10 | R | 1.5 | 1 | 3 | 1.5 | 38 | 2015 | 68521 |
2 | U | 0.66 | 1 | 3 | 2.5 | 16 | 2190 | 101500 |
5 | S | 0.66 | 1 | 3 | 1.5 | 22 | 1750 | 117650 |
6 | S | 1 | 2 | 3 | 2.5 | 8 | 2190 | 266000 |
9 | S | 0.75 | 2 | 3 | 3 | 6 | 3450 | 601500 |
1 | U | 0.5 | 0 | 2 | 1.5 | 31 | 1064 | 39800 |
3 | S | 0.75 | 2 | 4 | 2.5 | 9 | 2540 | 401500 |
7 | R | 5 | 3 | 5 | 2.5 | 4 | 4200 | 782000 |
4 | S | 0.66 | 2 | 3 | 2 | 8 | 1980 | 201500 |
10 | R | 14 | 1 | 3 | 2 | 17 | 1865 | 119500 |
2 | U | 0.75 | 1 | 3 | 2 | 21 | 1750 | 88420 |
5 | S | 0.5 | 2 | 3 | 2 | 15 | 1700 | 188500 |
6 | S | 0.5 | 1 | 3 | 2 | 8 | 2045 | 231100 |
9 | S | 0.5 | 2 | 3 | 2.5 | 15 | 2700 | 485200 |
1 | U | 0.75 | 1 | 3 | 2 | 29 | 1550 | 48999 |
3 | S | 0.5 | 2 | 4 | 2 | 13 | 2390 | 366500 |
7 | R | 9 | 2 | 3 | 2 | 17 | 2050 | 356420 |
4 | S | 0.25 | 1 | 2 | 2 | 8 | 1830 | 157650 |
10 | R | 0.5 | 0 | 2 | 1 | 36 | 1450 | 49874 |
2 | U | 0.5 | 2 | 3 | 2.5 | 9 | 1800 | 91640 |
5 | S | 0.75 | 1 | 3 | 2 | 12 | 2015 | 179500 |
6 | S | 0.5 | 2 | 3 | 2.5 | 4 | 1950 | 189500 |
9 | S | 0.5 | 2 | 4 | 2.5 | 4 | 2888 | 532800 |
1 | U | 0.4 | 1 | 3 | 2 | 16 | 2012 | 52100 |
3 | S | 0.5 | 2 | 3 | 2.5 | 7 | 2450 | 399500 |
7 | R | 4 | 2 | 3 | 2.5 | 37 | 3450 | 388600 |
4 | S | 0.4 | 2 | 3 | 2.5 | 2 | 2200 | 175800 |
10 | R | 8 | 2 | 3 | 2 | 21 | 2220 | 95400 |
2 | U | 0.5 | 1 | 2 | 1.5 | 15 | 1995 | 96888 |
5 | S | 1 | 2 | 3 | 2 | 36 | 2100 | 171630 |
6 | S | 0.75 | 2 | 3 | 2 | 7 | 2750 | 207500 |
9 | S | 0.75 | 2 | 4 | 3 | 2 | 3120 | 577900 |
1 | U | 0.25 | 0 | 2 | 2 | 14 | 1011 | 49875 |
3 | S | 0.5 | 2 | 2 | 2 | 6 | 2120 | 247800 |
7 | R | 22 | 3 | 4 | 3.5 | 3 | 3890 | 497500 |
4 | S | 0.66 | 2 | 4 | 2.5 | 4 | 2100 | 205000 |
10 | R | 2.5 | 0 | 3 | 1 | 35 | 1090 | 77000 |
2 | U | 0.4 | 1 | 2 | 2 | 4 | 1900 | 91400 |
5 | S | 0.25 | 1 | 2 | 2 | 3 | 1040 | 152800 |
6 | S | 1 | 2 | 4 | 3 | 7 | 3850 | 401500 |
9 | S | 0.5 | 2 | 3 | 3 | 1 | 2950 | 505000 |
1 | U | 0.4 | 1 | 2 | 2 | 25 | 1000 | 58700 |
3 | S | 0.5 | 1 | 3 | 2.5 | 2 | 2850 | 285235 |
7 | R | 75 | 2 | 3 | 2 | 15 | 2740 | 675500 |
4 | S | 0.25 | 1 | 2 | 2 | 4 | 1850 | 188760 |
10 | R | 11 | 2 | 3 | 2 | 5 | 2950 | 171680 |
2 | U | 0.75 | 1 | 2 | 2 | 7 | 1640 | 84600 |
5 | S | 0.8 | 2 | 3 | 2 | 2 | 1800 | 166900 |
6 | S | 0.75 | 3 | 4 | 2.5 | 7 | 3200 | 366900 |
9 | S | 0.5 | 2 | 3 | 2 | 9 | 2400 | 411960 |
1 | U | 0.5 | 2 | 3 | 2 | 17 | 2200 | 68900 |
3 | S | 0.5 | 2 | 4 | 2.5 | 8 | 3300 | 297600 |
7 | R | 11 | 2 | 4 | 3 | 3 | 4350 | 524700 |
4 | S | 0.4 | 2 | 3 | 2 | 12 | 1800 | 181500 |
10 | R | 4 | 1 | 2 | 2 | 37 | 1750 | 88520 |
2 | U | 0.5 | 0 | 3 | 1.5 | 32 | 1490 | 79450 |
5 | S | 0.5 | 1 | 3 | 2 | 17 | 1500 | 151960 |
6 | S | 1 | 2 | 3 | 2 | 11 | 2175 | 302900 |
9 | S | 0.5 | 2 | 3 | 2.5 | 11 | 2550 | 489650 |
1 | U | 0.25 | 0 | 2 | 1.5 | 12 | 850 | 64995 |
3 | S | 1 | 2 | 3 | 2.5 | 6 | 2752 | 400500 |
7 | R | 18 | 2 | 5 | 3 | 14 | 4540 | 711000 |
4 | S | 0.5 | 1 | 2 | 2 | 9 | 1590 | 172450 |
10 | R | 2 | 1 | 2 | 1.5 | 24 | 1275 | 81400 |
Create the Descriptive Statistics for "Price" and "SQFT" - Include a 95% Confidence Test on the Population Mean
1.Bring data in to excel sheet.
2.go to data tab--> choose data analysis from analyze option --? choose descriptive statistics
3.
Produce a Scatterplot for Price (Dependent) and SQFT (Independent) (PLACE BELOW)
1.Select sqft and price column and insert scatter diagram in excel like below
Righ click inside the scatter and add trendline , choose linear and get the lineare regression equation as well
use a Population Standard Deviation of $172,000 for your Z-test (i.e., we are determing if the mean has increased from the previous year). |
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