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maks197457 [2]
3 years ago
10

Wanda spent 4 1/8 hours studying last night. She spent 1/3 of the time studying math. How much time did she spend studying math?

Mathematics
1 answer:
julia-pushkina [17]3 years ago
4 0

Answer:

1\frac{3}{8} hours, or 1 hour and 22.5 minutes

Step-by-step explanation:

Wanda spends a third of her 4\frac{1}{8} hours studying math. Let us multiply these two fractions to find how much time she spent. Our first step would be to convert the mixed number into an improper fraction, as mixed numbers are hard to manipulate with:

4\frac{1}{8} =\\\frac{33}{8}

Now let us multiply:

\frac{1}{3} *\frac{33}{8} =\\\frac{11}{8}

If we wish, we can convert this into a mixed number:

\frac{11}{8}= \\1\frac{3}{8}

or, 1 hour and 22.5 minutes.

<u>Therefore, Wanda spent </u>1\frac{3}{8}<u> hours, or 1 hour and 22.5 minutes studying math.</u>

<u />

<em>I hope this helps! Let me know if you have any questions :)</em>

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3 years ago
20,10,5, ...<br> Find the 9th term.
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Step-by-step explanation:

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At a recent county fair, you observed that at one stand people's weight was forecasted, and were surprised by the accuracy (with
MatroZZZ [7]

Answer:

a)Slope: \hat \beta_1 =\frac{7625.9}{1248.9}=6.106

Intercept: \hat \beta_o = 157.955 -6.106 (69.686)=-267.548

b) r=\frac{7625.9}{\sqrt{[1248.9][94228.8]}}=0.657  

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Step-by-step explanation:

Data given and definitions

The correlation coefficient is a "statistical measure that calculates the strength of the relationship between the relative movements of two variables". It's denoted by r and its always between -1 and 1.  

The sum of squares "is the sum of the square of variation, where variation is defined as the spread between each individual value and the grand mean"    

When we conduct a multiple regression we want to know about the relationship between several independent or predictor variables and a dependent or criterion variable.  

n=110, \sum x_i y_i = \sum (X-\bar X)(Y-\bar Y) =7625.9,\sum x^2_i=\sum (x-\bar x)^2 =1248.9 , sum y^2_i=\sum(y-\bar y)^2 =94228.8  

\sum Y_i =17375 , \sum X_i = 7665.5

Part a

The slope is given by this formula:

\hat \beta_1 =\frac{\sum (x-\bar x) (y-\bar y)}{\sum (x-\bar x )^2}

If we replace we got:

\hat \beta_1 =\frac{7625.9}{1248.9}=6.106

We can find the intercept with the following formula

\hat \beta_o = \bar y -\hat \beta_1 \bar x

We can find the average for x and y like this:

\bar X=7665.5/110 =69.686, \bar y= 17375/110=157.955

And replacing we got:

\hat \beta_o = 157.955 -6.106 (69.686)=-267.548

Part b

In order to calculate the correlation coefficient we can use this formula:  

r=\frac{\sum (x-\bar x)(y-\bar y) }{\sqrt{[\sum (x-\bar x)^2][\sum(y-\bar y)^2]}}  

For our case we have this:  

n=110, \sum x_i y_i = \sum (X-\bar X)(Y-\bar Y) =7625.9,\sum x^2_i=\sum (x-\bar x)^2 =1248.9 , sum y^2_i=\sum(y-\bar y)^2 =94228.8  

So we can find the correlation coefficient replacing like this:

r=\frac{7625.9}{\sqrt{[1248.9][94228.8]}}=0.657  

And the determination coeffecient is r^2 = 0.657^2 =0.432  

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