Use the Following Information for Following Questions

question 25

Multiple Choice

Use the following information for following questions .
Data were recorded for 117 months on a household's gas bill (in dollars) and the average monthly temperatures for its neighborhood. The mean monthly temperature was 48.7° F with a standard deviation of 20.6. The mean gas bill price was $81.20 with a standard deviation of 66.5. The correlation coefficient between monthly temperature and gas bill price is −0.92.
-Determine the correct value of the slope for the linear model that predicts gas bill price from monthly temperature and interpret it in context.

• A)The slope is −2.97.For every one degree increase in monthly temperature, the gas bill price is predicted to decrease by $2.97.
• B)The slope is −2.97.For every one dollar increase in gas bill price, the monthly temperature is predicted to decrease by 2.97°.
• C)The slope is −0.28.For every one degree increase in monthly temperature, the gas bill price is predicted to decrease by $0.28.
• D)The slope is −0.28.For every one dollar increase in gas bill price, the monthly temperature is predicted to decrease by 0.28°.

Understand the anatomy and functions of the human eye including its structure and the role each part plays in vision.

Grasp the phenomena of visual perception including color vision, visual acuity, and common vision conditions.

Explain the process of accommodation in vision and its implications on visual acuity across different distances.

Recognize the importance of the sensory experience in everyday life and how it is prioritized or adapted by the sensory system.

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Definitions:

Coefficient Of Determination

A metric in statistics that evaluates the percentage of variability in the outcome variable that can be explained by the predictor variable(s).

SSE

Sum of Squared Errors, a measure used in statistics to quantify the discrepancy between data points and a model's predictions.

SSR

Sum of Squares due to Regression, a measure indicating how well a regression model fits the data.

SSE

The Sum of Squared Errors represents the difference between observed data points and the values predicted by a model.