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WINSTONCH [101]
2 years ago
8

How does confidence interval affect margin of error.

Mathematics
1 answer:
Vesnalui [34]2 years ago
8 0
The width of the confidence interval is twice the margin of error. The lower bound of the confidence interval is the observed score minus the margin of error;
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At Pizza hut, 9% of the pizzas made last week had extra cheese. if 27 pizzas had extra cheese, how many pizzas in all were made
notka56 [123]

To solve this, we need to figure out how many times 9% goes into 100%.

100% / 9% = 11.111

Since <u>9% of the pizzas equaled 27 pizzas</u>, we can <u>multiply 27 by the amount 11.111</u> to get the total number of pizzas (100%).

27 * 11.11 = 299.997, rounded to the nearest whole number is 300 Pizzas

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3 years ago
Find the volume of the prism
frozen [14]

Answer:

96 in^3

Step-by-step explanation:

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Vlada [557]

Answer:

a

Step-by-step explanation:

4 0
3 years ago
Which is equivalent to 23·2-5?<br> A) 2-15 <br> B) 2-8 <br> Eliminate<br> C) 2-2 <br> D) 22
bulgar [2K]
2^3 * 2^-5 = 2^(3 + (-5) = 2^(3 - 5) = 2^-2
4 0
3 years ago
In a study relating the degree of warping, in mm, of a copper plate (y) to temperature in °C (x), the following summary statisti
kipiarov [429]

Answer:

The error sum of squares is SSE=12.97

The regression sum of squares is SSR=6.430

The total sum of squares is SST=19.4

Step-by-step explanation:

Linear regression is a way "to modeling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables)".

Regression estimates are "used to describe data and to explain the relationship between one dependent variable and one or more independent variables"

The linear model is given by the following equation Y=mx +b, where y is the dependent variable, x the independent variable, m the slope and b the intercept.

For this case we have the following info given:

\sum_{i=1}^n (x_i -\bar x)^2 =98775

\sum_{i=1}^n (y_i -\bar y)^2 =19.4

\bar x =26.36

\bar y =0.5188

n= 40  represent the sample size

\sum_{i=1}^n (x_i -\bar x)(y_i -\bar y) =796.94

The total sum of squares is given by this formula:

SST= \sum_{i=1}^n (y_i -\bar y)^2 =19.4

And from the formulas for a simple regression, we need to calculate the slope for the regression like this:

m=\frac{\sum_{i=1}^n (x_i -\bar x)(y_i -\bar y)}{\sum_{i=1}^n (x_i -\bar x)^2}

And if we replace the values given we have:

m=\frac{796.94}{98775}=0.008068

We have another useful equation in order to find the sum of squares for the regression, given by:

SSR=m* \sum_{i=1}^n (x_i -\bar x)(y_i -\bar y)=0.008068*796.94=6.430

And we know this equivalence:

SST= SSR+SSE

And solving for the sum of squares for the error we have:

SSE=SST-SSR=19.4-6.430=12.97

So then we have the final solutions:

The error sum of squares is SSE=12.97

The regression sum of squares is SSR=6.430

The total sum of squares is SST=19.4

6 0
3 years ago
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