Please help with the last question, my "n" is too small, can anyone explain the possibility or why?

3) For each condition list the means, standard deviations, and standard error for the conditions for time and money spent. Please note that means you should have several sets of M, SD, and SE. Be sure you name the sets of means, sd, and se different things so you can use them later.

```{r descriptives}

M1=tapply(dataset$money, list(dataset$where_bought, dataset$who_bought), mean)

M1

SD1=tapply(dataset$money, list(dataset$where_bought, dataset$who_bought), sd)

SD1

L1=tapply(dataset$money, list(dataset$where_bought, dataset$who_bought), length)

L1

SE1=SD1/sqrt(L1)

SE1

M2=tapply(dataset$time, list(dataset$where_bought, dataset$who_bought), mean)

M2

SD2=tapply(dataset$time, list(dataset$where_bought, dataset$who_bought), sd)

SD2

L2=tapply(dataset$time, list(dataset$where_bought, dataset$who_bought), length)

L2

SE2=SD2/sqrt(L2)

SE2

```

4) Which condition appears to have the best model fit using the mean as the model (i.e. smallest error) for time?

For time, since the "Purchased in store from different retailers" has the smallest error, it might be the best model

fit using the mean.

5) What are the df for each condition?

```{r df}

df1=L1-1

df1

df2=L2-1

df2

```

6) What is the confidence interval (95%) for the means?

```{r conf-interval}

##money

M1+1.96*SE1

M1-1.96*SE1

##time

M2+1.96*SE2

M2-1.96*SE2

```

7) Use the MOTE library to calculate the effect size for the difference between money spent for the following comparisons (that means you'll have to do this twice):

```{r MOTE}

install.packages("MOTE")

library(MOTE)

##Store versus online when bought at the same retailer

effect1=d.ind.t(34.88091, 25.13133, 2.738662, 3.256608, 50, 50,a=0.05)

effect1$d

##Store versus online when bought at a different retailer

effect2=d.ind.t(34.70278, 25.38076, 3.051608, 2.805442, 50, 50,a=0.05)

effect2$d

```

8) What can you determine about the effect size in the experiment - is it small, medium or large?

Both effects are larger than 0.8, I think it will be considered as large.

9) How many people did we need in the study for each comparison?

```{r pwr}

##Store versus online when bought at the same retailer

library(pwr)

pwr.t.test(n=NULL, d=effect1$d, sig.level=0.05, power=0.80, type="two.sample", alternative="two.sided")

We need people if we want to compare store versus online when bought at the same retailer .

##Store versus online when bought at a different retailer

pwr.t.test(n=NULL, d=effect2$d, sig.level=0.05, power=0.80, type="two.sample", alternative="two.sided")

We needpeople if we want to compare store versus online when bought at the different retailer .