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3 years ago

What is the correlation coefficient for the data shown in the

Mathematics

2 answers:

zaharov [31]3 years ago

8 0

Answer:

The number of times a player has golfed in one’s lifetime is compared to the number of strokes it takes the player to complete 18 holes. The correlation coefficient relating the two variables is –0.26.

Which best describes the strength of the correlation, and what is true about the causation between the variables?

It is a weak negative correlation, and it is not likely causal.

It is a weak negative correlation, and it is likely causal.

It is a strong negative correlation, and it is not likely causal.

It is a strong negative correlation, and it is likely causal.

Step-by-step explanation:

Crank3 years ago

4 0

Answer: r= -1

Step-by-step explanation:

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Fraction less than 5/6 and has a denominator of 8

lesya692 [45]

6/8, 5/8, 4/8, 3/8, 2/8, 1/8

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4 years ago

emily is buting gifts for 20 people at work.The boxes for the gifts are 6 inches long,6 inches wide ,and 6 inches high .How much

kvasek [131]

Answer:

360 inches or 30 feet

Step-by-step explanation:

6x3=18

18×20=360 inches

or...

360÷12=30 to get amount in feet.

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3 years ago

Consider this scenario: The population of a city increased steadily over a ten-year span. The following ordered pairs show the p

vova2212 [387]

Answer:

Regression function: $y=1986.406+0.0059x$

The function predicts that population will reach 14,000 in year 2068.

Step-by-step explanation:

We have to determine a function $y=b_0+b_1x_1$ by applying linear regression. The data we have is 5 pair of points which relates population to year.

According to the simple regression model (one independent variable), if we minimize the error between the model (the linear function) and the points given, the parameters are:

$b_0=\bar{y}+b_1\bar{x}\\\\b_1=\frac{\sum\limits^5_{i=1} {(x_i-\bar x)(y_i-\bar y)}}{\sum\limits^5_{i=1} {(x_i-\bar x)^2}}$

We start calculating the average of x and y

$\bar x=\frac{2500+2650+3000+3500+4200}{5}=\frac{15850}{5}=3170\\\\ \bar y=\frac{2001+2002+2004+2007+2011}{5}=\frac{10025}{5}=2005$

The sample covariance can be calculated as

$\sum\limits^5_{i=1} {(x_i-\bar x)(y_i-\bar y)}=(2500-3170)(2001-2005)+(2650-3170)(2002-2005)+(3000-3170)(2004-2005)+(3500-3170)(2007-2005)+(4200-3170)(2011-2005)\\\\\sum\limits^5_{i=1} {(x_i-\bar x)(y_i-\bar y)}=2680+1560+170+660+6180\\\\ \sum\limits^5_{i=1} {(x_i-\bar x)(y_i-\bar y)}=11250$

The variance of x can be calculated as

$\sum\limits^5_{i=1} {(x_i-\bar x)^2}=(2500-3170)^2+(2650-3170)^2+(3000-3170)^2+(3500-3170)^2+(4200-3170)^2\\\\\sum\limits^5_{i=1} {(x_i-\bar x)^2}=448900+270400+28900+108900+1060900\\\\\sum\limits^5_{i=1} {(x_i-\bar x)^2}=1918000$

Now we can calculate the parameters of the regression model

$b_1=\frac{\sum\limits^5_{i=1} {(x_i-\bar x)(y_i-\bar y)}}{\sum\limits^5_{i=1} {(x_i-\bar x)^2}}=\frac{11250}{1918000}=0.005865485 \\\\ b_0=\bar{y}+b_1\bar{x}=2005-0.005865485*3170=1986.406413$

The function then become:

$y=1986.406+0.0059x$

With this linear equation we can predict when the population will reach 14,000:

$y=1986.406+0.0059(14,000)=1986.406+82.117=2068.523$

6 0

3 years ago

Convert 534.3% to a decimal

bazaltina [42]

If you would like to convert 534.3% to a decimal, you can do this using the following steps:

534.3% = 534.3 / 100 = 5.343

The correct result would be 5.343.

6 0

3 years ago

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$30 to $50 what is the markup percent

n200080 [17]

The markup percent is $15

8 0

3 years ago