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3 years ago

What is the LCD of 2/7 and 9/10

2 answers:

8 0

Assuming LCD means Lowest Common Denominator
Assuming by "and" you mean adding the two

Well, you go by the multiples of 7;
7, 14, 21, 28, 35, 42, 49, 56, 63, 70
Multiples of 10;
10, 20, 30, 40, 50, 60, 70, 80, 90, 100

Straight away you can see the lowest common multiple is 70,
Now to go about adding them,
2/7 * 10 (both sides) , 20/70
9/10 * 7 (both sides) , 63/70

Then you add the two; 20/70 + 63/70 - now we can add them because they share the same denominator
20/70 + 63/70 = 83/70 = 1 whole 13/70 (simplified)

The LCD is 70

Quick tip, to find a common multiple (not necessarily the lowest) multiply the two denominators.

Zigmanuir [339]3 years ago

7 0

The least common denominator or LCD is 70 because for
10=10,20,30,40,50,60,70,80
7=7,14,21,28,35,42,49,56,63,70,77
and 70 is the least common denominator of 10 and 7 so 70 ims your answer

You might be interested in

An island has a popularity of 1500 and is growing at a rate of 3% per year. what will the popularity be after 6 years?

gulaghasi [49]

Step-by-step explanation:

Growth in population each year = 3%(1500)

= 3/100 × 1500

= 3 × 15

= 45

Therefore, the growth in population in 6 years

= 6 × 3%(1500)

= 6 × 45

= 270

Hence, the population after 6 years = 1500 + 270

= 1770

4 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 _(0.02) less than the significance level 0.05, there is 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!