Spss
X= attitude towards the city
Y= duration of residence in that city
H0 = rXY=0 i.e there is NO relationship between the 2 continuous variables X and Y
Ha ≠0, there is a relationship between the 2 continuous variables X and Y
Analyze -> correlate -> bivariate
The SPSS output indicates that rXY has a value of .936. Clearly this value of rxy seems different from zero. However the question that arises is whether this value of rxy is statistically different from zero at 95% level of confidence.
There is a significant relationship between these two variables at 95% confidence as p-value is at .000 which is below 0.05. Hence we are therefore unable to retain H0 and thus we accept HA. Thus, we can infer at 95% level of confidence that in this sample, there is indeed a significant relationship between the two variables X and Y (i.e rXY is indeed different from zero). Assuming that this sample is a good representation of the target population, we extend this inference even to the target population. Hence, even in the target population there is indeed a significant positive relationship between the two variables X and Y (i.e rXY is indeed different from zero).
2. Multiple regression write up y= attitude towards the city of residence (dependent variable) x1 = duration of residence in the city (1st independen variable) x2= importance associated with the weather in the city (2nd IV)
The initial model is: Y = β1 + β2.X1+ β3.X2
Step 1:
H0: R2= 0 i.e none of the independent variables X1 and X2 have a significant relationship with the dependent variable Y i.e. both β2 and β3 are equal to zero
HA: R2 ≠ 0 i.e At least one of the independent variables X1 and X2 have a significant relationship with the dependent variable Y i.e. at least one of the two,β2 and β3 are NOT equal to zero
The SPSS output indicates that R2 has a value of .945. This value does seem different from zero. However, we need to confirm is whether this R2 is indeed different from zero, in
Y= duration of residence in that city
H0 = rXY=0 i.e there is NO relationship between the 2 continuous variables X and Y
Ha ≠0, there is a relationship between the 2 continuous variables X and Y
Analyze -> correlate -> bivariate
The SPSS output indicates that rXY has a value of .936. Clearly this value of rxy seems different from zero. However the question that arises is whether this value of rxy is statistically different from zero at 95% level of confidence.
There is a significant relationship between these two variables at 95% confidence as p-value is at .000 which is below 0.05. Hence we are therefore unable to retain H0 and thus we accept HA. Thus, we can infer at 95% level of confidence that in this sample, there is indeed a significant relationship between the two variables X and Y (i.e rXY is indeed different from zero). Assuming that this sample is a good representation of the target population, we extend this inference even to the target population. Hence, even in the target population there is indeed a significant positive relationship between the two variables X and Y (i.e rXY is indeed different from zero).
2. Multiple regression write up y= attitude towards the city of residence (dependent variable) x1 = duration of residence in the city (1st independen variable) x2= importance associated with the weather in the city (2nd IV)
The initial model is: Y = β1 + β2.X1+ β3.X2
Step 1:
H0: R2= 0 i.e none of the independent variables X1 and X2 have a significant relationship with the dependent variable Y i.e. both β2 and β3 are equal to zero
HA: R2 ≠ 0 i.e At least one of the independent variables X1 and X2 have a significant relationship with the dependent variable Y i.e. at least one of the two,β2 and β3 are NOT equal to zero
The SPSS output indicates that R2 has a value of .945. This value does seem different from zero. However, we need to confirm is whether this R2 is indeed different from zero, in