In R there is a dataset called diamonds that contains measurements of about 500 diamonds sold in the US. There are three variables present: price (price in US dollar), carat (weight of the diamond), and table (width of top of diamond relative to the widest point). The attached image is a screenshot of the R dataset with the regression table and all that. ANSWER THIS QUESTION IN WORDS: Discuss the regression between the variables table and price. You should address the explanatory variable, response variable, correlation, and sign. You should interpret the slope, the t and p-value, and how much is explained by the response.
In R there is a dataset called diamonds that contains measurements of about 500 diamonds sold in the US. There are three variables present: price (price in US dollar), carat (weight of the diamond), and table (width of top of diamond relative to the widest point). The attached image is a screenshot of the R dataset with the regression table and all that. ANSWER THIS QUESTION IN WORDS: Discuss the regression between the variables table and price. You should address the explanatory variable, response variable, correlation, and sign. You should interpret the slope, the t and p-value, and how much is explained by the response.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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In R there is a dataset called diamonds that contains measurements of about 500 diamonds sold in the US. There are three variables present: price (price in US dollar), carat (weight of the diamond), and table (width of top of diamond relative to the widest point). The attached image is a screenshot of the R dataset with the regression table and all that.
ANSWER THIS QUESTION IN WORDS:
Discuss the regression between the variables table and price. You should address the explanatory variable, response variable, correlation , and sign. You should interpret the slope, the t and p-value, and how much is explained by the response.
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my_model <- 1m(price - carat, data = diamonds)
summary (my_model)
##
## Call:
## 1m (formula = price - carat, data = diamonds)
##
## Residuals:
##
Min
1Q Median
Мax
##
-7583
-918
-15
648
7521
##
## Coefficients:
##
Estimate Std. Error t value Pr(>|t|)
## (Intercept)
-2414
143
-16.9
<2e-16 ***
## carat
7818
141
55.5
<2e-16 ***
## ---
## Signif. codes:
O ***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1590 on 498 degrees of freedom
## Multiple R-squared: 0.861, Adjusted R-squared: 0.86
## F-statistic: 3.07e+03 on 1 and 498 DF, p-value: <2e-16
The correlation between carat and price was H. For each increase of one carat, we estimate that the
price of diamonds will increase by $H.
We [do/do not] have significant evidence of this relationship, because the t-score for the slope is [ ], which has
a p-value of -. This means that the data we observed would be [likely/unlikely] to come from variables
with a true correlation of [-.
Overall, the regression was [significant/not signficant], because of our F-statistic of [H with a p-value of
The explanatory variable explained [% of the variance in the response variable.
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...](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F6010a0ae-7a62-4910-937d-5ce2ea4f371d%2Fe602dd2e-4b2a-4420-b211-88c708af8ea6%2Fxl8o1q_processed.png&w=3840&q=75)
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my_model <- 1m(price - carat, data = diamonds)
summary (my_model)
##
## Call:
## 1m (formula = price - carat, data = diamonds)
##
## Residuals:
##
Min
1Q Median
Мax
##
-7583
-918
-15
648
7521
##
## Coefficients:
##
Estimate Std. Error t value Pr(>|t|)
## (Intercept)
-2414
143
-16.9
<2e-16 ***
## carat
7818
141
55.5
<2e-16 ***
## ---
## Signif. codes:
O ***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1590 on 498 degrees of freedom
## Multiple R-squared: 0.861, Adjusted R-squared: 0.86
## F-statistic: 3.07e+03 on 1 and 498 DF, p-value: <2e-16
The correlation between carat and price was H. For each increase of one carat, we estimate that the
price of diamonds will increase by $H.
We [do/do not] have significant evidence of this relationship, because the t-score for the slope is [ ], which has
a p-value of -. This means that the data we observed would be [likely/unlikely] to come from variables
with a true correlation of [-.
Overall, the regression was [significant/not signficant], because of our F-statistic of [H with a p-value of
The explanatory variable explained [% of the variance in the response variable.
99+
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12:43 AM
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