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den301095 [7]
1 year ago
9

Data on average high temperatures​ (in degrees​ Fahrenheit) in July and precipitation​ (in inches) in July for 48 cities is used

to find a regression line and correlation coefficient. PRECIP​ = 2.0481​ + 0.0067 HIGH R​ (correlation coefficient)​ = 0.0358 ​(1) Give the value of the slope of the regression line. ​(2) Identify the predictor variable in this context. ​(3) Identify the response variable in this context. ​(4) Clearly interpret the numerical value of the slope in the context of the​ problem, namely connecting​ "precipitation" and​ "average high​ temperature". ​(5) Predict the amount of precipitation​ (two places past the​ decimal) for a city that has an average high temperature in July of 87.31 degrees Fahrenheit.
Mathematics
1 answer:
wel1 year ago
5 0

Answer:

Explained below.

Step-by-step explanation:

The regression equation to predict amount of precipitation​ (in inches) in July from the average high temperatures​ (in degrees​ Fahrenheit) in July is as follows:

PRECIP​ = 2.0481​ + 0.0067 HIGH

(1)

The value of the slope of the regression line is, 0.0067.

(2)

The predictor variable in this context is the average high temperatures​ (in degrees​ Fahrenheit) in July.

(3)

The response variable in this context is the amount of precipitation​ (in inches) in July.

(4)

The slope of a regression line is average rate of change in the dependent variable with one unit change in the independent variable.

The slope here is 0.0067.

This value implies that the average rate of change in the amount of precipitation​ (in inches) in July increases by 0.0067 inches with every 1°F increase in the average high temperatures​.

(5)

Compute the mount of precipitation for a city that has an average high temperature in July of 87.31°F  as follows:

PRECIP​ = 2.0481​ + 0.0067 HIGH

             = 2.0481​ + 0.0067 × 87.31°F

             = 2.633077

             ≈ 2.63 inches.

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For this case we have the following equation:
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d (4) = 48 + 20

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2 years ago
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Determine if each of the following sets is a subspace of ℙn, for an appropriate value of n. Type "yes" or "no" for each answer.
xxMikexx [17]

Answer:

1. Yes.

2. No.

3. Yes.

Step-by-step explanation:

Consider the following subsets of Pn given by

1.Let W1 be the set of all polynomials of the form p(t)=at^2, where a is in ℝ.

2.Let W2 be the set of all polynomials of the form p(t)=t^2+a, where a is in ℝ.

3. Let W3 be the set of all polynomials of the form p(t)=at^2+at, where a is in ℝ.

Recall that given a vector space V, a subset W of V is a subspace if the following criteria hold:

- The 0 vector of V is in W.

- Given v,w in W then v+w is in W.

- Given v in W and a a real number, then av is in W.

So, for us to check if the three subsets are a subset of Pn, we must check the three criteria.

- First property:

Note that for W2, for any value of a, the polynomial we get is not the zero polynomial. Hence the first criteria is not met. Then, W2 is not a subspace of Pn.

For W1 and W3, note that if a= 0, then we have p(t) =0, so the zero polynomial is in W1 and W3.

- Second property:

W1. Consider two elements in W1, say, consider a,b different non-zero real numbers and consider the polynomials

p_1 (t) = at^2, p_2(t)=bt^2.

We must check that p_1+p_2(t) is in W1.

Note that

p_1(t)+p_2(t) = at^2+bt^2  = (a+b)t^2

Since a+b is another real number, we have that p1(t)+p2(t) is in W1.

W3. Consider two elements in W3. Say p_1(t) = a(t^2+t), p_2(t)= b(t^2+t). Then

p_1(t) + p_2(t) = a(t^2+t) + b(t^2+t) = (a+b) (t^2+t)

So, again, p1(t)+p2(t) is in W3.

- Third property.

W1. Consider an element in W1 p(t) = at^2and a real scalar b. Then

bp(t) = b(at^2) = (ba)t^2).

Since (ba) is another real scalar, we have that bp(t) is in W1.

W3. Consider an element in W3 p(t) = a(t^2+t)and a real scalar b. Then

bp(t) = b(a(t^2+t)) = (ba)(t^2+t).

Since (ba) is another real scalar, we have that bp(t) is in W3.

After all,

W1 and W3 are subspaces of Pn for n= 2

and W2 is not a subspace of Pn.  

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2 years ago
To test whether or not there is a difference between treatments A, B, and C, a sample of 12 observations has been randomly assig
Keith_Richards [23]

Answer:

1. Null hypothesis: \mu_{A}=\mu_{B}=\mu_{C}

Alternative hypothesis: Not all the means are equal \mu_{i}\neq \mu_{j}, i,j=A,B,C

2. D. 36

3. C. 34

4. B. 1.059

5. B. 8.02

Step-by-step explanation:

Analysis of variance (ANOVA) "is used to analyze the differences among group means in a sample".

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"

Part 1

The hypothesis for this case are:

Null hypothesis: \mu_{A}=\mu_{B}=\mu_{C}

Alternative hypothesis: Not all the means are equal \mu_{i}\neq \mu_{j}, i,j=A,B,C

Part 2

In order to find the mean square between treatments (MSTR), we need to find first the sum of squares and the degrees of freedom.

If we assume that we have p groups and on each group from j=1,\dots,p we have n_j individuals on each group we can define the following formulas of variation:  

SS_{total}=\sum_{j=1}^p \sum_{i=1}^{n_j} (x_{ij}-\bar x)^2

SS_{between}=SS_{model}=\sum_{j=1}^p n_j (\bar x_{j}-\bar x)^2

SS_{within}=SS_{error}=\sum_{j=1}^p \sum_{i=1}^{n_j} (x_{ij}-\bar x_j)^2

And we have this property

SST=SS_{between}+SS_{within}

We need to find the mean for each group first and the grand mean.

\bar X =\frac{\sum_{i=1}^n x_i}{n}

If we apply the before formula we can find the mean for each group

\bar X_A = 27, \bar X_B = 24, \bar X_C = 30. And the grand mean \bar X = 27

Now we can find the sum of squares between:

SS_{between}=SS_{model}=\sum_{j=1}^p n_j (\bar x_{j}-\bar x)^2

Each group have a sample size of 4 so then n_j =4

SS_{between}=SS_{model}=4(27-27)^2 +4(24-27)^2 +4(30-27)^2=72

The degrees of freedom for the variation Between is given by df_{between}=k-1=3-1=2, Where  k the number of groups k=3.

Now we can find the mean square between treatments (MSTR) we just need to use this formula:

MSTR=\frac{SS_{between}}{k-1}=\frac{72}{2}=36

D. 36

Part 3

For the mean square within treatments value first we need to find the sum of squares within and the degrees of freedom.

SS_{within}=SS_{error}=\sum_{j=1}^p \sum_{i=1}^{n_j} (x_{ij}-\bar x_j)^2

SS_{error}=(20-27)^2 +(30-27)^2 +(25-27)^2 +(33-27)^2 +(22-24)^2 +(26-24)^2 +(20-24)^2 +(28-24)^2 +(40-30)^2 +(30-30)^2 +(28-30)^2 +(22-30)^2 =306

And the degrees of freedom are given by:

df_{within}=N-k =3*4 -3 = 12-3=9. N represent the total number of individuals we have 3 groups each one with a size of 4 individuals. And k the number of groups k=3.

And now we can find the mean square within treatments:

MSE=\frac{SS_{within}}{N-k}=\frac{306}{9}=34

C. 34

Part 4

The test statistic F is given by this formula:

F=\frac{MSTR}{MSE}=\frac{36}{34}=1.059

B. 1.059

Part 5

The critical value is from a F distribution with degrees of freedom in the numerator of 2 and on the denominator of 9 such that we have 0.01 of the area in the distribution on the right.

And we can use excel to find this critical value with this function:

"=F.INV(1-0.01,2,9)"

And we will see that the critical value is F_{crit}=8.02

B. 8.02

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Answer:

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