D :)
Because the y values are the same, all we need to take into account is the difference in the length of the x values.
Answer:
Step-by-step explanation:
f(x)=2(x-2)²-5
f(x)=2(x²-4x+4)-5
f(x)=2x²-8x+3
Here you go :) . . . . ..
Answer:
0.5
Step-by-step explanation:
Solution:-
- The sample mean before treatment, μ1 = 46
- The sample mean after treatment, μ2 = 48
- The sample standard deviation σ = √16 = 4
- For the independent samples T-test, Cohen's d is determined by calculating the mean difference between your two groups, and then dividing the result by the pooled standard deviation.
Cohen's d = ![\frac{u2 - u1}{sd_p_o_o_l_e_d}](https://tex.z-dn.net/?f=%5Cfrac%7Bu2%20-%20u1%7D%7Bsd_p_o_o_l_e_d%7D)
- Where, the pooled standard deviation (sd_pooled) is calculated using the formula:
![sd_p_o_o_l_e_d =\sqrt{\frac{SD_1^2 +SD_2^2}{2} }](https://tex.z-dn.net/?f=sd_p_o_o_l_e_d%20%3D%5Csqrt%7B%5Cfrac%7BSD_1%5E2%20%2BSD_2%5E2%7D%7B2%7D%20%7D)
- Assuming that population standard deviation and sample standard deviation are same:
SD_1 = SD_2 = σ = 4
- Then,
![sd_p_o_o_l_e_d =\sqrt{\frac{4^2 +4^2}{2} } = 4](https://tex.z-dn.net/?f=sd_p_o_o_l_e_d%20%3D%5Csqrt%7B%5Cfrac%7B4%5E2%20%2B4%5E2%7D%7B2%7D%20%7D%20%3D%204)
- The cohen's d can now be evaliated:
Cohen's d = ![\frac{48 - 46}{4} = 0.5](https://tex.z-dn.net/?f=%5Cfrac%7B48%20-%2046%7D%7B4%7D%20%3D%200.5)