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3 years ago

The faces of a rectangular solid are square. always sometimes never

Mathematics

2 answers:

Rudik [331]3 years ago

5 0

The answer is sometimes.... sorry i don’t feel like explaining lol

Sonja [21]3 years ago

4 0

Answer:

sometimes

Step-by-step explanation:

We can consider the example of a cuboid.

It is a rectangular solid whose all faces are made of rectangles.

But if we will consider a example of cube; which is also a kind of rectangular solid whose all faces are made up of square.

so we can get some solids whose faces are other than squares as well and some whose faces are squares.

Hence, we could say that:

The faces of a rectangular solid are sometimes square.

You might be interested in

Write down 3 integers, all less than 25 whose range is 8 and mean is 13

EleoNora [17]

Answer:

9, 13, 17

Step-by-step explanation:

The difference between the greatest value and the least is 8, the range.

Add the three numbers to total 39, then divide by 3 to get the mean, 13.

6 0

2 years ago

Listed below are annual data for various years. The data are weights (metric tons) of imported lemons and car crash fatality ra

anyanavicka [17]

Answer:

Because the P-value is _(<u>0.02) less </u> than the significance level 0.05, there <u> is </u> sufficient evidence to support the claim that there is a linear correlation between lemon imports and crash fatality rates for a significance level of α=0.05.

C. The results suggest that imported lemons cause car fatalities.

Step-by-step explanation:

Hello!

The study variables are:

X₁: Weight of imported lemons.

X₂: Car crash fatality rate.

The objective is to test if the imported lemons affect the occurrence of car fatalities. To do so you are asked to use a linear correlation test.

I've made a Scatterplot with the given data, it is attached to the answer.

To be able to use the parametric linear correlation you can use the parametric test (Person) or the nonparametric test Spearman. For Person, you need your variables to have a bivariate normal distribution. Since one of the variables is a discrete variable (ratio of car crashes) and the sample is way too small to make an approximation to a normal distribution, the best test to use is Spearman's rank correlation.

This correlation coefficient (rs) takes values from -1 to 1

If rs = -1 this means that there is a negative correlation between the variables

If rs= 1 this means there is a positive correlation between the variables

If rs =0 then there is no correlation between the variables.

The hypothesis is:

H₀: There is no linear association between X₁ and X₂

H₁: There is a linear association between X₁ and X₂

α: 0.05

To calculate the Spearman's correlation coefficient you have to assign ranks to the observed values of each variable, from the smallest to the highest). Then you have to calculate the difference (d)between the ranks and the square of that difference (d²). (see attachment)

The formula for the correlation coefficient is:

$rs= 1 - \frac{6* (sum of d^2)}{(n-1)n(n+1)}$

$rs= 1 - \frac{6* (40)}{4*5*6}$

rs= -1

For this value of the correlation coefficient, the p-value is 0.02

Since the p-value (0.02) is less than the significance level (0.05) the decision is to reject the null hypothesis. In other words, there is a linear correlation between the imported lemons and the car crash fatality ration, this means that the modification in the lemon import will affect the car crash fatality ratio.

Note: the correlation coefficient is negative, so you could say that there is a correlation between the variables and this is negative (meaning that when the lemon import increases, the car crash fatality ratio decreases)

I hope it helps!