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olga55 [171]
2 years ago
6

Jenny drank 2.4. liters of water. How

Mathematics
1 answer:
bazaltina [42]2 years ago
4 0

Answer:

2400 milliliters

Step-by-step explanation:

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The number of Apples John has is 8
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Messy Marvin buys a bag of peanuts and eats 20% of whatever is in the bag each day. If there are 256 peanuts left in the bag aft
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Point C ∈
Korolek [52]

Answer:

  • AC: 11 cm
  • CB: 22 cm

Step-by-step explanation:

<u>Given</u>:

  AC + CB = 33

  CB = 2AC

<u>Find</u>:

  AC, CB

<u>Solution</u>:

Using substitution, we have ...

  AC +2AC = 33

  AC = 33/3 = 11

  CB = 2·11 = 22

AC = 11 cm; CB = 22 cm.

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3 years ago
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3. Please answer the following questions: a. What criterion in regression are we trying to minimize when we estimate our regress
Damm [24]

Answer:

Regression : Impact of y on x ; Regression Function : y = a + b(x)

Optimum Least square criteria, with assumptions - linear parameters, homoscedasticity, no autocorrelation, no multicollinearity.

Step-by-step explanation:

Regression is a statistical tool used to calculate the impact of independent/ explanatory variable (x) on dependent variable (y). Eg : Impact of temperature on ice cream sales.

Population regression function is linear function, establishing a hypothetical theoretical relationship between independent variable x & dependent variable y.

Regression Equation is : y = a + b(x)  ;

where : a is the autonomous value of dependent variable y ; b represents the coefficient of change in dependent variable y due to change in independent variable x.

Optimum Least Square method criterion is used while estimating regression model. The criteria minimises the squares of residuals, where residuals are the differences between observed values & estimated values. It is used as a measure of how well the estimated regression line fits the actual data.

Assumptions made while an OLS regression model are : Linear in parameters, Homoscedasticity (variables constant errors), No autocorrelation (errors correlation), No multicollinearity ( independent variables' correlation)

8 0
3 years ago
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