A function lin_lsqfit. mcapable of calculating the least square estimates of the regression constants aand b. The function has the form [a, b] = lin_lsqfit(x, y), where x and y are the input vectors with the data points. The output variables a, b. You can use any programming approach you want. However, you cannot use any built-in regression functions in MATLAB, such as polyfit.

< xcy ≅ < xby

step-by-step explanation:

in δcax and δbax,

so, δcax ≅ δbax (side-angle-side or sas)

since δcax ≅ δbax, we can conclude < acx ≅ < abx

in δacy and δaby,

so, δacy ≅ δaby (side-angle-side or sas)

since δacy ≅ δaby, we can conclude < acy ≅ < aby

now,

< acy ≅ < aby

=> < acx + < xcy ≅ < abx + < xby

=> < acx + < xcy ≅ < acx + < xby   (since < acx ≅ < abx already proved above)

subtracting < acx from both sides, we get

< acx + < xcy -< acx ≅ < acx + < xby -< acx

cancelling out < acx and -< acx from both sides, we get

< xcy ≅ < xby (proved)

2: 10

step-by-step explanation:

5

step-by-step explanation: