pls help i don’t understand

Step-by-step explanation:

b)

make it braintliest please

Answer:

z = -3

Step-by-step explanation:

Step 1: Simplify both sides of the equation.

z+7=−z+1

Step 2: Add z to both sides.

z+7+z=−z+1+z

2z+7=1

Step 3: Subtract 7 from both sides.

2z+7−7=1−7

2z=−6

Step 4: Divide both sides by 2.

2z   =  −6

2      2

z=−3

Answer:

Why you delete my answer

Step-by-step explanation:

The most helpful statistical process in determining critical variables in a student's success in a statistics class would be option b: regression with success in statistics class as the dependent variable.

In regression analysis, we use one variable to predict another. The variable that we are trying to predict is called the dependent variable, while the variable that we are using to make the prediction is called the independent variable. In this case, we are trying to determine which factors (independent variables) are most critical in determining a student's success in a statistics class (dependent variable).

By using regression analysis with success in statistics class as the dependent variable, we can identify which independent variables (such as study habits, previous math experience, etc.) have the strongest relationship with success in the class. These independent variables would then be considered the critical variables in determining student success in the class.

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Answer:

11:27 am.

Step-by-step explanation:

The time which has passed when Tom overtakes Hazel is after Hazel has walked 1 hr 15 minutes and in that time Hazel has walked

1.25 * 4.8

= 6 km.

So, Tom runs 6 km in 10:15 - 9.39 = 36 minutes

= 0.6 hrs.

So, Tom's speed is 6/0.6 = 10 km/hr.

,

and Tom will take 18/10 = 1.8 hours to finish the route,

= 1 hr 48 minutes

So he finishes the route at

9:39 + 1:48

= 11:27 am.

The best predicted value of y when x = 94 is 11.1

from the question, we have the following parameters that can be used in our computation:

Temperature, x 72 85 91 90 88 98 75 100 80

Absencees, y 3 7 10 10 8 15 4 15 5

Using the least squares, we have the following summary

The regression equation is

y = mx + b

Where

m =  SP/SSX = 330.22/736.22 = 0.44854

b = MY - bMX = 8.56 - (0.45*86.56) = -30.26773

So, we have

y = 0.44x - 30.27

When x = 94, we have

y = 0.44 * 94 - 30.27

y = 11.1

Hence, the prediction is 11.1

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Question

Provide an appropriate response, The data bolow are the temperatures on randomly chosen days duning the summer in one city and the number of employee absences

Which is the best predicted value of y when x = 94

Temperature, x 72 85 91 90 88 98 75 100 80

Absencees, y 3 7 10 10 8 15 4 15 5

Answer:180

Step-by-step explanation:

1-------60

2-------120

3-------x

4------240

n-------60*n

so, 3-----60*3=180

Answer: 165

Step-by-step explanation:

5 X( 15+18) = 5 X 15 + 5 X 18 = 75 + 90 = 165.

Distributive law = a.(b + c) = a. b + a. c

Answer:

The Coefficients in this expression are 10 and 3, while the constants are 5 and 1.

Step-by-step explanation:

Coefficients = The number that is in front of the variable.

Constant = Any number without a variable.

(Variables are the letters that you may find in an equation or function.)

Hope this helps you :)

Answer:

The coefficient are 10 and  3

The constants are 5 and 1

Step-by-step explanation:

A coefficient is the number in front of any known term.

A constant is a number by itself.

So the coefficient are 10 and  3

The constants are 5 and 1

Answer:

-4,-5?

Step-by-step explanation:

Answer:

(-4, -5)

Step-by-step explanation:

Answer:

Not sure, is there a problem or only the rant? lol. I'd appreciate my first brainliest though.

Step-by-step explanation:

Answer:

sorry I also dont understand

Answer:

Step 1: Locate and underline the whole numbers place (2) then look at the digit to the right (6):

2.6

Step 2: In this case, the digit to the right (6) is 5 or above. So, we add 1 to the whole numbers place (2). The digit(s) after the (2) are replaced by zeros (or dropped). Thus, we get 3 as the answer.

In short: 2.6 rounded to the nearest whole number is 3.

Answer:

yes

Step-by-step explanation:

Answer:

the answer is -2

Step-by-step explanation:

i used my notebook

The item that is not an item in the income statement is b. Furniture & Fixture as it is considered a fixed asset and is reported on the balance sheet instead.

The income statement, also known as the profit and loss statement, provides a summary of a company's revenues, expenses, gains, and losses over a specific period. It helps to assess the financial performance of a business. The income statement typically includes various items such as revenues, cost of goods sold, operating expenses, interest income or expense, and other gains or losses.

a. Discount allowed is a revenue item that represents the discounts given to customers as an incentive for early payment or other reasons. It is usually reported as a deduction from sales revenue.

c. Furniture & Fixture is not typically included in the income statement. Instead, it is considered a non-operating or non-recurring item and is generally classified as a fixed asset on the balance sheet. Fixed assets represent long-term investments made by a company for its operations.

d. Discount received is also not an item in the income statement. It represents the discounts received by a company from its suppliers for early payment or other reasons. Similar to discount allowed, it is usually reported as a deduction from the respective expense account.

In summary, b. Furniture & Fixture is the item that is not included in the income statement. It is considered a fixed asset and is reported on the balance sheet instead.

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Answer:

       To change a mixed number to an improper fraction quickly we can multiply the whole number by the denominator, add the numerator and then write that over the original denominator.

Homoskedasticity and heteroskedasticity are terms used to describe the variability of errors in a multiple linear regression model.

To check for heteroskedasticity in a multiple linear regression model, there are several diagnostic tests and graphical methods that can be used: Residual plot, Breusch-Pagan test, White's test, Goldfeld-Quandt test,

Homoskedasticity refers to a situation where the variance of the errors (residuals) is constant across all levels of the independent variables. This means that the spread of the residuals is the same throughout the range of predicted values. In other words, the variability of the errors is consistent and does not change systematically.

Heteroskedasticity, on the other hand, occurs when the variance of the errors is not constant across all levels of the independent variables. In this case, the spread of the residuals tends to vary as the predicted values change. Heteroskedasticity implies that the variability of the errors is not consistent and may exhibit patterns or trends.

To check for heteroskedasticity in a multiple linear regression model, there are several diagnostic tests and graphical methods that can be used: Residual plot, Breusch-Pagan test, White's test, Goldfeld-Quandt test,

Residual plot: Plotting the residuals against the predicted values or the independent variables can reveal patterns or trends in the spread of the residuals. If the spread of the residuals appears to fan out or there is a clear pattern, it suggests the presence of heteroskedasticity.

Breusch-Pagan test: This is a statistical test that formally tests for heteroskedasticity in the residuals. The test assesses the relationship between the squared residuals and the independent variables. If the test result is statistically significant, it indicates the presence of heteroskedasticity.

White's test: Similar to the Breusch-Pagan test, White's test also examines the relationship between the squared residuals and the independent variables. It is another statistical test commonly used to detect heteroskedasticity in regression models.

Goldfeld-Quandt test: This test divides the data into two groups based on a selected independent variable and compares the variances of the residuals between the two groups. If the variances significantly differ, it suggests the presence of heteroskedasticity.

By conducting these tests and examining the graphical plots, one can assess whether heteroskedasticity is present in the multiple linear regression model. If heteroskedasticity is detected, it may be necessary to address the issue by using heteroskedasticity-robust standard errors, transforming variables, or considering alternative regression techniques designed to handle heteroskedastic data.

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Answer: f(2) = -7   , g(3) = -29

Step-by-step explanation: this problem can be solved via substitution

-2(2) - 3 = -4 - 3 = -7

-3(3)^2 - 2 = -3(9) - 2 = -27 - 2 = -29

Answer:

i think it is 20 percent

Step-by-step explanation:

Answer:

y >or equal to 1/3x+1

hope that can help

Buying 8 ounce jar of maple syrup for $8.95 is the better buy.

Unit rate is defined as the amount of a quantity in a single unit of the other quantity.

For example, the distance one travels in km per hour is a unit rate of the distance covered in a single hour.

Price for 8 ounce jar of maple syrup = $8.95

Price for 1 ounce jar of maple syrup = 8.95 / 8 = $1.119

Price for 10 ounce jar of maple syrup = $12.0

Price for 1 ounce jar of maple syrup = 12 / 10 = $1.20

Therefore the unit price of 8 ounce jar is $1.119 and the unit price for the 10 ounce jar of maple syrup is $1.20.

Difference in the unit prices = $1.200 - $1.119

                                               = $0.081

                                               ≈ $0.08

Since the unit price of 10 ounce jar of maple syrup is higher than that of 8 ounce jar, 8 ounce jar of maple syrup is the better buy.

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The range would be the most appropriate measure of spread in this case because it takes into account the extreme values of $300 and $1,300 and provides a clear measure of the difference between them.

To measure the amount of money college students spend on rent per month, the most appropriate measure of spread would be the range. The range is the simplest measure of spread and is calculated by subtracting the lowest value from the highest value in a data set. In this case, the range would be $1,300 - $300 = $1,000.

The median would not be the best choice in this scenario because it only represents the middle value in a data set. It does not take into account extreme values like the $1,300 rent expense.

Standard deviation would not be the most appropriate measure of spread in this case because it calculates the average deviation of each data point from the mean. However, it may not accurately represent the spread when extreme values like the $1,300 rent expense are present.

The interquartile range (IQR) would not be the best choice either because it measures the spread of the middle 50% of the data set. It does not consider extreme values and would not accurately represent the range of rent expenses in this scenario.

In summary, the range would be the most appropriate measure of spread in this case because it takes into account the extreme values of $300 and $1,300 and provides a clear measure of the difference between them.

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Answer:

1 11/16

1. 2 + 2/3 = 2 2/3

2 2/3 x 2 2/3 = 7 1/9

12 / 7 1/9 = 1 11/16

Answer:

10.6

Step-by-step explanation:

To find the difference between 2 numbers, subtract the bigger one from the smaller one. In this case, 8.4-(-2.2) = 8.4 + 2.2 = 10.6

To construct and store a 4 x 2 matrix filled row-wise with the given values in R, you can use the following code:

# Create the matrix

myMatrix <- matrix(c(4.3, 3.1, 8.2, 9.2, 3.2, 0.9, 1.6, 6.5), nrow = 4, ncol = 2, byrow = TRUE)

This code creates a matrix called "myMatrix" with 4 rows and 2 columns, filled row-wise with the provided values.

To overwrite the second column of the matrix with the numbers 8, 9, 11, and 17 in that order, you can use the following code:

# Overwrite the second column

myMatrix[, 2] <- c(8, 9, 11, 17)

This code selects the second column of the matrix using the indexing notation [, 2] and assigns the new values using the c() function. The second column is replaced with the numbers 8, 9, 11, and 17.

Finally, to save the updated matrix to an object named "BruceLee", you can use the following code:

# Save the updated matrix

BruceLee <- myMatrix

Now the updated matrix with the overwritten second column is stored in the object "BruceLee" for further use.

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Answer:

x=7

Step-by-step explanation:

Step #1- Set the two lines equal to each other

10+12=x+15

Step #2- Solve for the line you have all values for

10+12=22

Step#3- Plug 22 back into the equation where the two lines are set equal to each other

22=x+15

Step #4- Solve for X algebraically

22=x+15

22-15=x

7=x

AnTHE ANSWER IS 2

Step-by-step explanation:

I JUS KNOW

The true statements about the wedding planning business are: (d) If 30 small tables are used, 5 large tables will be needed. and (e) If no large tables are used, 45 small tables would be needed.

The given parameters are

Number of guests = 180

Small tables = 4 people per seat

Large tables = 12 people per seat

Let the number of small tables be x and the number of large tables be y

So, we have the following equation

4x + 12y = 180

From the graph, we have the following ordered pairs:

(30, 5), (0, 15) and (45, 0)

The above ordered pairs mean that:

If 30 small tables are used, 5 large tables will be needed.

If no small tables are used, 15 small tables would be needed.

If no large tables are used, 45 small tables would be needed.

Hence, the true statements about the wedding planning business are: (d) If 30 small tables are used, 5 large tables will be needed. and (e) If no large tables are used, 45 small tables would be needed.

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Answer:

Step-by-step explanation:

a.4 hours drive 240 miles, so the speed is 240÷4=60 miles per hour

b.1 days have 24 hours, then, it will go 60 ×24 = 1440 miles in 1 days

Answer:

1440

Step-by-step explanation:

240+4= 60

distance = rt

r= rate

t = time

IN ONE DAY

60 times 24= 1440

(24 for 24 hours)