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d1i1m1o1n [39]
1 year ago
11

Each year a nationally recognized publication conducts its 'Survey of America's Best Graduate and Professional Schools.' An acad

emic advisor wants to predict the typical starting salary of a graduate at a top business school using GMAT score of the school as a predictor variable. Total GMAT scores range from 200 to 800. A simple linear regression of SALARY versus GMAT using 25 data points yields the regression equation given below. y = 228x - 92,040 Give an interpretation of the y-intercept. The value has no practical interpretation since a GMAT of 0 is nonsensical and outside the range of the sample data. We estimate the base SALARY of graduates of a top business school to be $-92,040. We estimate SALARY to decrease $92,040 for every 1-point increase in GMAT. We expect to predict SALARY to within 2(92040) = $184,080 of its true value using GMAT in a straight-line model.
Mathematics
1 answer:
Lady_Fox [76]1 year ago
3 0

Answer:

The correct answer is the value has no practical interpretation since a GMAT of 0 is nonsensical and outside the range of the sample data.

Step-by-step explanation:

Solution

Given that:

We define Define Y : dependent variable ( Starting salary of a graduate at a top business school )

Thus,

X : independent variable ( GMAT score )

So,

The Required linear regression equation is stated below:

y = 228 x - 92,040

Here,

The y intercept is = - 92040

The Interpretation of the y - intercept  is defined as:

The value has no practical interpretation since a GMAT of 0 is nonsensical and outside the range of the sample data .

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Answer:

the answer is C: 2,500.80

Step-by-step explanation:

                                                                             

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T=10 months/year
I=PRT
Since R is a missing term, we will solve for R using this formula: R=I/PT
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The quality control manager at a light bulb factory needs to estimate the mean life of a batch (population) of light bulbs. We a
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<em>a)95%  confidence intervals for the population mean of light bulbs in this batch</em>

(325.5 ,374.5)

b)

<em>The calculated value Z = 4 > 1.96 at 0.05 level of significance</em>

<em>Null hypothesis is rejected </em>

<em>The manufacturer has not right to take the average life of the light bulbs is 400 hours.</em>

Step-by-step explanation:

Given sample size n = 64

Given  mean of the sample x⁻ = 350

Standard deviation of the Population σ = 100 hours

The tabulated value Z₀.₉₅ = 1.96

<em>95%  confidence intervals for the population mean of light bulbs in this batch</em>

<em></em>(x^{-} - Z_{\frac{\alpha }{2} } \frac{S.D}{\sqrt{n} } , x^{-} + Z_{\frac{\alpha }{2} }\frac{S.D}{\sqrt{n} } )<em></em>

<em></em>(350 - 1.96\frac{100}{\sqrt{64} } , 350 + 1.96\frac{100}{\sqrt{64} } )<em></em>

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b)

<u><em>Explanation</em></u>:-

Given mean of the Population μ = 400

Given sample size n = 64

Given  mean of the sample x⁻ = 350

Standard deviation of the Population σ = 100 hours

<u><em>Null hypothesis</em></u> : H₀:The manufacturer has right to take the average life of the light bulbs is 400 hours.

μ = 400

<u><em>Alternative Hypothesis: H₁:</em></u> μ ≠400

<u><em>The test statistic </em></u>

Z = \frac{x^{-}-mean }{\frac{S.D}{\sqrt{n} } }

Z = \frac{350 -400}{\frac{100}{\sqrt{64} } }

|Z| = |-4|

The tabulated value   Z₀.₉₅ = 1.96

The calculated value Z = 4 > 1.96 at 0.05 level of significance

Null hypothesis is rejected.

<u><em>Conclusion:</em></u>-

The manufacturer has not right to take the average life of the light bulbs is 400 hours.

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