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Pavlova-9 [17]
3 years ago
10

Expand the expression log (xy/4) please show your work trying to lean it

Mathematics
1 answer:
Elza [17]3 years ago
4 0
Log xy/4 =
log xy - log 4 =
log x + log y - log 4
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Q sorry if I am wrong
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You basically look at the bar graph on the right and see which statement is true. Brainliest and thank you!
wel

Answer:

D

Step-by-step explanation:

To check this answer, you could obviously graph the points, but without graphing, you can tell the line would be straight because each x value is multiplied by the same number to get the corresponding y value.

3 x 5 = 15, 4 x 5 = 20, etc. This shows linear growth. If each x value were multiplied by twice itself (3 x 6, 4 x 8, and so on), you wouldn't have a straight line because the number that x is multiplied by changes depending on the value of x.

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2 years ago
When n is small (less than 30), how does the shape of the t distribution compare to the normal distribution?
Anna [14]

When n is small (less than 30), how does the shape of the t distribution compare to the normal distribution then"it is flatter and wider than the normal distribution."

<h3>What is normal distribution?</h3>

The normal distribution explains a symmetrical plot of data around the mean value, with the standard deviation defining the width of the curve. It is represented graphically as "bell curve."

Some key features regarding the normal distribution are-

  • The normal distribution is officially known as the Gaussian distribution, but the term "normal" was coined after scientific publications in the nineteenth century demonstrated that many natural events emerged to "deviate normally" from the mean.
  • The naturalist Sir Francis Galton popularized the concept of "normal variability" as the "normal curve" in his 1889 work, Natural Inheritance.
  • Even though the normal distribution is a crucial statistical concept, the applications in finance are limited because financial phenomena, such as expected stock-market returns, do not fit neatly within a normal distribution.
  • In fact, prices generally follow a right-skewed log-normal distribution with fatter tails.

As a result, relying as well heavily on the a bell curve when forecasting these events can yield unreliable results.

To know more about the normal distribution, here

brainly.com/question/23418254

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6 0
1 year ago
Is the relation a function? Why or why not?<br><br> {(–3, –2), (–1, 0), (1, 0), (5, –2)}
tankabanditka [31]

Answer:

yes, the given relation is a function.

Step-by-step explanation:

The given relation is

{(–3, –2), (–1, 0), (1, 0), (5, –2)}

A relation is called function if each element of the domain is paired with exactly one element of the range.

It means for each value of x there exist a unique value of y.

In the given relation for each value of x there exist a unique value of y.

Therefore the required solution is yes and this relation is a function.

5 0
3 years ago
Real Estate One conducted a recent survey of house prices for properties located on the shores of Tawas Bay. Data on 26 recent s
Ivanshal [37]

Answer:

Step-by-step explanation:

Hello!

Given the data for the variables:

Y: Selling price of a house on the shore of Tawas Bay

X₁: Number of bathrooms of a house on the shore of Tawas Bay.

X₂: Square feet of a house on the shore of Tawas Bay.

X₃: Number of bedrooms of a house on the shore of Tawas Bay.

The multiple regression model is Y= α + β₁X₁ + β₂X₂ + β₃X₃ + εi

a. Using software I've entered the raw data and estimated the regression coefficients:

^α= a= -5531.01

Represents the mean selling price of the houses when 0 bathrooms, 0 square feet and 0 bedrooms.

^β₁= b₁= -1386.21

Represents the modification of the mean selling price of the houses when the number of bathrooms increases in one unit and the square feet and number of bedrooms remain unchanged.

^β₂= b₂= 60.28

Represents the modification of the mean selling price of the houses when the square feet increase in one unit and the number of bathrooms and bedrooms remain unchanged.

^ β₃= b₃= 54797.08

Represents the modification of the mean selling price of the houses when the number of bedrooms increase in one unit and the number of bathrooms and square feet of the houses remain unchanged.

^Y= -5531.01 -1386.21X₁ + 60.28X₂ + 54797.08X₃

b)

R²= 0.55

R²Aj= 0.49

The coefficient of determination gives you an idea of how much of the variability of the dependent variable (Y) is due to the explanatory variables. Each time you add another explanatory variable to the regression the coefficient increases regarding of real contribution of the new variable. This could lead to thinking (wrongly) that the new variables are good to explain the dependent variable.  

The adjusted coefficient of determination is a correction made to the raw coefficient of determination to have a more unbiased estimation of the effect the independent variables have over the dependent variable.

⇒ As you can see both coefficient are around 50%, which means that these explanatory variables

c)

The standard error estimate, this is the estimate of the population variance of the errors. In the ANOVA is represented by the Mean Square of the errors (MME)

Se²= MME= 3837640577.01

Se= 61948.6931

d) and f)

For the hypotheses tests for each slope the t- and p-values are:

α: 0.05

β₁: t_{H_0}= \frac{b_1-\beta_1 }{Sb_1} t= -0.06; p-value: 0.9528 ⇒ Do not reject H₀, the test is not significant.

β₂: t_{H_0}= \frac{b_2-\beta_2 }{Sb_2} t= 2.56; p-value: 0.0180 ⇒ Reject H₀, the test is significant.

β₃: t_{H_0}= \frac{b_3-\beta_3 }{Sb_3} t= 2.28; p-value: 0.0326 ⇒ Reject H₀, the test is significant.

e)

H₀: β₁= β₂= β₃

H₁: At least one βi is different from the others ∀ i=1, 2, 3

α: 0.05

F= 9.03

p-value: 0.0004

⇒ Reject H₀, the test is significant.

I hope it helps!

5 0
3 years ago
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