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Nikolay [14]
1 year ago
10

The first digit in any number must be 1, 2, 3, 4, 5, 6, 7, 8, or 9 because we do not write numbers such as 15 as 015. While it i

s reasonable to think that for most real-life data each digit occurs with equal frequency so that each digit 1, 2, ..., 9 has probability 1/9 of being the first digit, this is not true. It is a surprising phenomenon that in many naturally occurring numbers and web-based data the first digit has a probability distribution known as Benford's law.
Benford's law, also called the first-digit law, states that in lists of numbers from many (but not all) real-life sources of data, the leading digit is distributed in a specific, non-uniform way.
Specifically, for d = 1, 2, 3, 4, 5, 6, 7, 8, 9, Benford's law states P(first digit is d) = log_10(1+(1/d)) . The distribution of the first digit according to Benford's law, calculated to 3 decimal places, is shown in the table below.
Benford's law
First Digit X 1 2 3 4 5 6 7 8 9
Probability .301 .176 .125 .097 .079 .067 .058 .051 .046
Benford's Law and the Equally Likely Model benford equally likely
The law is named after physicist Frank Benford, who stated it in 1938, although it had been previously stated by Simon Newcomb in 1881. A surprising variety of data from the natural sciences, social affairs, and business obeys Benford's law.
This result has been found to apply to a wide variety of data sets, including electricity bills, street addresses, stock prices, population numbers, death rates, lengths of rivers, physical and mathematical constants, and processes described by power laws (which are very common in nature).
Numbers that are assigned, such as social security numbers and zip codes, or data with a fixed maximum, such as deductible contributions to individual retirement accounts, or randomly generated numbers, do not follow Benford's law.
The figure below shows how the distribution of the first digit of various naturally occurring and web-based data compares with Benford's law.
benford natural data web data
Figure information. Earthquakes: depth in km of 248,915 quakes, 1989-2009; source National Earthquake Information Center, United States Geological Survey. Minnesota lakes: size in acres of approx. 1100 lakes; source Wikipedia. Births: number of births in each county of the United States (approximately 3200), 2010; source: US Census Bureau. Diggs: total number of diggs for each of the top 1000 diggers at digg.com; source: socialblade.com.
Question 1:
(1a). What is the expected value of the first digit when the first digit follows Benford's law?
expected value (Use 3 decimal places).
(1b). What is the expected value of the first digit when the possible first digits are equally likely?
expected value (Use 3 decimal places).
(1c). What is the standard deviation of the first digit when the first digit follows Benford's Law?
Mathematics
1 answer:
Pie1 year ago
4 0

Answer:

143 i think

Step-by-step explanation:

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A food distributor is being sued for racial discrimination because 12% of newly hired candidates are not white when 53% of all a
Musya8 [376]

Answer:

Check Explanation

Step-by-step explanation:

A) The null hypothesis would be that the proportion of newly hired candidates that are not white is not significantly different from the proportion of the applicants that are not white & there is no significant evidence that the company's hiring practices are discriminatory.

Mathematically,

H₀: μ₀ = 0.53

And the alternative hypothesis would be that there is a significant difference between the proportion of newly hired candidates that are not white is not significantly different from the proportion of the applicants that are not white. More specifically, that the proportion of newly hired candidates that are not white is significantly less than the proportion of applicants that are not white & there is significant evidence that the company's hiring practices are indeed discriminatory.

Mathematically,

Hₐ: μ₀ < 0.53

B) The two errors that can come up in this hypothesis testing include -

Type I error: We reject the null hypothesis because we obtain that the proportion of newly hired candidates that are not white is significantly less than the proportion of applicants that are not white and conclude that there is indeed significant evidence that the company's hiring practices are discriminatory when in reality, there is no significant difference and hence, no discrimination.

Type II error: We accept the null hypothesis (fail to reject the null hypothesis) because we obtained that there is no significant difference between the proportion of newly hired candidates that are not white & th proportion of applicants that are not white and conclude that there is no discrimination in the company's hiring practices when in reality, there is significant difference in the stated proportions above and significant evidence that there is indeed significant evidence that the company's hiring practices are discriminatory.

C) The power of the test increases as the significance level reduces. This is because t-statistic increases as significance level reduces.

D) The standard error of the mean used in computing the t-score is given as

σₓ = (σ/√n)

It is evident that as the value of n increases, the standard error reduces and this widens the effect of the test, hence, the power of the test increases.

Hope this Helps!!!

7 0
1 year ago
The chance that a positive response is obtained from Chicago for program 1 is %. The chance that a positive response is obtained
sergejj [24]
 the chance of a positive response for Program 2 given that the individual is from Los Angeles is 69.7.
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7 0
1 year ago
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Select the coordinates of two points on the line y= 8
notsponge [240]
The answer for this is (0,8) and (8,0)
7 0
2 years ago
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Mya is a performance athlete. She wants to use a function to represent the number of calories that she burns while training, whe
Lerok [7]

Answer:

The actual number of calories that Mya burned when she

trained for 30 minutes is 240.

Step-by-step explanation:

We have an exponential function that is the best fit for the number of calories Mya burns, in function of time (minutes).

The function is:

y=45.3e^{0.0548x}

Where y: calories burned, and x: training time.

Whenever we use regression models, we have a real value and a predicted value. The difference between them, the measure we want to minimize when we adjust with this typo of models, is called residual.

In the table is shown as one of the columns.

For example, for x=10 minutes, we have a real value of 30 and a predicted value of 78.36, so the residual becomes e=30-78.36=-48.36.

e=y-\hat y=30-78.36=-48.36

For x=30 minutes, we have only the residual, that has a value of 5.53. That means that the predicted value, which we can calculate, is 5.53 below the real value.

The predicted value for x=30 is:

y(30)=45.3e^{0.0548*30}=45.3*5.18=234.47

Using the equation for the residual e, we can calcualte the actual number of calories that Mya burned in 30 minutes:

e=y-\hat y=y-234.47=5.53\\\\y=234.47+5.53\\\\y=240

4 0
1 year ago
Which shows the correct substitution of the values a, b, and c from the equation 0 = – 3x2 – 2x + 6 into the quadratic formula?
Gemiola [76]
Hello : 
the discriminat of each quadratic equation : ax²+bx+c=0 ....(a <span>≠ 0) is :
</span><span>Δ = b² -4ac
1 )  </span>Δ > 0  the equation has two reals solutions : x =  (-b±√Δ)/2a
2 ) Δ = 0 : one solution : x = -b/2a
3 ) Δ <span>< 0 : no reals solutions
in this exercice : -3x²-2x+6 =0.....a = -3   b = -2     c =6
calculate : </span>Δ............................
7 0
1 year ago
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