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m_a_m_a [10]
2 years ago
5

Consider the function f(x)=−34x+2f(x)=−34x+2 . What is f(4)f(4)?

Mathematics
2 answers:
dimaraw [331]2 years ago
6 0
The answer is x=-134
zlopas [31]2 years ago
4 0
The answer would be x=-134
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A recent article in Business Week listed the "Best Small Companies." We are interested in the current results of the companies'
Sindrei [870]

Answer:

(i) The estimated regression equation is;

\hat y ≈ 1.6896 + 0.0604·X

The coefficient of 'X' indicates that \hat y increase by a multiple of 0.0604 for each million dollar increase in sales, X

(ii) The estimated earnings for the company is approximately $4.7096 million

(iii) The standard error of estimate is approximately 29.34

The high standard error of estimate indicates that individual mean do not accurately represent the population mean

(iv) The coefficient of determination is approximately 0.57925

The coefficient of determination indicates that the probability of the coordinate of a new point of data to be located on the line is 0.57925

Step-by-step explanation:

The given data is presented as follows;

\begin{array}{ccc}Sales \ (\$million)&&Earning \ (\$million) \\89.2&&4.9\\18.6&&4.4\\18.2&&1.3\\71.7&&8\\58.6&&6.6\\46.8&&4.1\\17.5&&2.6\\11.9&&1.7\end{array}

(i) From the data, we have;

The regression equation can be presented as follows;

\hat y = b₀ + b₁·x

Where;

b₁ = The slope given as follows;

b_1 = \dfrac{\Sigma(x_i - \overline x) \cdot (y_i - \overline y)}{\Sigma(x_i - \overline x)^2}

b₀ = \overline y - b₁·\overline x

From the data, we have;

{\Sigma(x_i - \overline x) \cdot (y_i - \overline y)} = 364.05

\Sigma(x_i - \overline x)^2} = 6,027.259

\overline y = 4.2

\overline x = 41.5625

∴ b₁ = 364.05/6,027.259 ≈ 0.06040059005

b₀ = 4.2 - 0.06040059005 × 41.5625 ≈ 1.68960047605 ≈ 1.69

Therefore, we have the regression equation as follows;

\hat y ≈ 1.6896 + 0.0604·X

The coefficient of 'X' indicates that the earnings increase by a multiple of 0.0604 for each million dollar increase in sales

(ii) For the small company, we have;

X = $50.0 million, therefore, we get;

\hat y = 1.6896 + 0.0604 × 50 = 4.7096

The estimated earnings for the company, \hat y = 4.7096 million

(iii) The standard error of estimate, σ, is given by the following formula;

\sigma =\sqrt{\dfrac{\sum \left (x_i-\mu  \right )^{2} }{n - 1}}

Where;

n = The sample size

Therefore, we have;

\sigma =\sqrt{\dfrac{6,027.259 }{8 - 1}} \approx 29.34

The standard error of estimate, σ ≈ 29.34

The high standard error of estimate indicates that it is very unlikely that a given mean value within the data is a representation of the true population mean

(iv) The coefficient of determination (R Square) is given as follows;

R^2 = \dfrac{SSR}{SST}

Where;

SSR = The Sum of Squared Regression ≈ 21.9884

SST = The total variation in the sample ≈ 37.96

Therefore, R² ≈ 21.9884/37.96 ≈ 0.57925

The coefficient of determination, R² ≈ 0.57925.

Therefore, by the coefficient of determination, the likelihood of a new introduced data point to located on the line is 0.57925

6 0
2 years ago
Rotate : energy :: stop:
vitfil [10]
Is that a question???????
4 0
2 years ago
Read 2 more answers
Xavier has $23.50 in his savings account. He deposits $35 every week. His mother also deposits $20 into the account every time X
andrezito [222]
Part A the coefficient is 20c and 35w, the variable is c and w representing cost and week. and the constant is 23.50.

Part B He would have 503.50 because <span><span>35</span><span>(12)</span></span>+60<span>=<span><span>420x</span>+<span>60+23.50 which would be 503.50

Part C The coefficient of the cost would change because you would be adding a 5 dollar increase!!!!!! done</span></span></span>
8 0
2 years ago
In a (blank),one ratio compares a part to a whole
Verdich [7]

Answer:

In a part-to-whole ratio, one ratio compares a part to a whole.

3 0
2 years ago
Rachel measures the lengths of a random sample of 100 screws. The mean length was 2.6 inches, with a standard deviation of 1.0 i
olga_2 [115]

Answer:

Step-by-step explanation:

Using the alternative hypothesis (µ < µ0),

To find the p-value with test statistic -1.25 and assuming a standard level of significance of 0.05, using a p value calculator, the p-value is 0.1057 which is great that 0.05. Thus, the results is not significant.

Using the p value calculation.

1. Check the left tailed z table as the test statistic is negative,

2. Then find the probabilitythat z is greater than your test statistic (look up your test statistic on the z-table- the value under 1.2 and 0.05 which is 0.8944

3. Then, find its corresponding probability, and subtract it from 1 to get your p-value- 1-0.8944 = 0.1056.

4 0
2 years ago
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