Appraised Value ($1,000’s) |
Selling Price ($1,000’s) |
|||
250 |
257 |
|||
190 |
250 |
|||
220 |
288 |
|||
185 |
162 |
|||
270 |
285 |
|||
500 |
541 |
|||
240 |
221 |
|||
The Excel regression output is shown below, with two cells missing.
Regression Statistics |
||||||||||||
Multiple R |
0.955 |
|||||||||||
R Squared |
0.912 |
|||||||||||
Adjusted R Squared |
0.895 |
|||||||||||
Standard Error |
39.055 |
|||||||||||
Observations |
7.000 |
|||||||||||
Coefficients |
Standard Error |
t Stat |
P-value |
Lower 95% |
Upper 95% |
|||||||
Intercept |
41.771 |
0.115 |
0.9127 |
-102.560 |
112.191 |
|||||||
Appraised Value ($1,000’s) |
0.147 |
7.203 |
0.0008 |
0.683 |
1.441 |
|||||||
Independent Variable:
Dependent Variable:
t crit / t test: [2 Mark]
p-value/alpha: [1 mark]
confidence interval: [1 marks]
Ftest/Fcrit: [2 marks]
a)
independent variable = Appraised Value ($1,000’s)
dependent value = Selling Price
Y = 41.771 + 0.147 * X
.
b)
slope = 0.147
so, if increase the appraised value by 1 unit, then selling price will be increase by 0.147
..........
c)
for X = 200
y = 41.771 + 0.147*200
=71.171
..
R Squared = 0.912
so, 91.2% of data is explained by independent variable appraised value of selling price
.................
t stat = 7.203
t critical = 2.571
t stat > t critical, slope significant
p value = 0.0008 >0.05 , slope significant
lower limit = 0.683 , upper limit = 1.441
CI does not contain 0 , slope is significant
................
Please revert back in case of any doubt.
Please upvote. Thanks in advance.
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