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Ad libitum [116K]
3 years ago
13

In a fifth grade class, 3/4 of the students like to go to the movies. Of the students who like to go to the movies, 2/3 of them

like action movies. Of the students who like to go to action movies, 4/5 of hem also like comedies. What fraction of the students in class like to go to action movies? What fraction of the students like to go to action and comedies?
Mathematics
2 answers:
ser-zykov [4K]3 years ago
7 0

Answer: 1) 1/2 students like to go to action movies,

2) 2/5 students like to go to action and comedies.

Step-by-step explanation:

Let the total number of students are x,

Thus, the number of students who like to go movies = 3/4 of total students = \frac{3x}{4}

The number of students who like to go action movies = 2/3 of students who like to go movies = 2/3 of 3x/4 = \frac{6x}{12} = x/2

Hence, the total fraction of students who like to go action movies = Number of students who like to go action movies / Total students

= \frac{x/2}{x}

= 1/2

Now, the number of student who like to go comedy movie = 4/5 of the students who like to go action movies

= 4/5 of x/2

= \frac{4x}{10}

= \frac{2x}{5}

Hence, the fraction of students who like to go comedy movies

=  Total students who like comedies/ Total students

= \frac{2x/5}{x}

= 2/5

scZoUnD [109]3 years ago
3 0
Hi, thank you for posting your question here at Brainly.

The process of solving this problem is as follows:

Students who like action movies: 3/4*2/3 = 1/2 of the fifth grade class students
Students who like comedies: 1/2*4/5 = 2/5 of the fifth grade class students


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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

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In conclusion Mrs. Owens has to make 3 pies and she is going to have 4 pieces left over.

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