#  Explore topic 13a
#  First load all of the functions we will use
source( "../gnrnd5.R")
source( "../gnrnd4.R")
source( "../pop_sd.R")
source("../assess_normality.R")
source( "../ci_known.R")

#  Topic 13a looks at generating a confidence
#  interval for a population mean when we 
#  know the population standard deviation.
#
#  Let us generate a population
gnrnd5(145209499902, 3306001054)
#  put the population into big_pop
big_pop <- L1

# We need to know the population standard deviation
hold_pop_sd <- pop_sd( big_pop )

#  let us say that we are taking a sample
#  of a certain size 
samp_size <- 38

#  then, just so that we can all get the same
#  sample, generate the index values for 
#  a sample of that size
key1 <- 702370001+ (samp_size-1)*100
gnrnd4(key1, 500000001)
L1
this_sample <- big_pop[ L1 ]
#  look at our sample
this_sample

#  we can find the sample mean and the sample standard
#  deviation

samp_mean <- mean( this_sample )
samp_sd   <- sd( this_sample )
samp_mean
samp_sd

# Now, we will use the population 
# standard deviation, the
# distribution of the sample means will be  
# Normal with the same mean as the population
# and standard deviation equal to the population
# standard deviation divided by the square root
# of the sample size.

#  then we make our confidence interval for 
#  some specified confidence level

conf_level <- 0.95
#  that means that we are missing 1-conf_level
#  which we will split in half, one half in
#  each tail
z_over_2 <- (1-conf_level)/2
z_over_2


# we can find 
# samp_mean +/- z(alpha_over2)*samp_sd/sqrt(samp_size)
# and that will be our confidence interval

z_score_low <- qnorm( z_over_2 )
z_score_low
z_score_high <- qnorm( z_over_2,
                       lower.tail=FALSE) # opposite of low val
z_score_high
st_error <- hold_pop_sd / sqrt( samp_size ) 
st_error
#  CI low value
samp_mean + z_score_low*st_error
#  CI high value
samp_mean + z_score_high*st_error

##### or we could have found the margin of error
MOE <- z_score_high*st_error 
MOE
#  and then found the limits for the 
#  confidence interval
samp_mean - MOE  # the low end
samp_mean + MOE  # the high end

###  of course all of this could be done via
#    our ci_known function
ci_known( hold_pop_sd, samp_size, samp_mean, conf_level)

##################################
#  we could try this at a different confidence
#  level.  Just alter the value in line 49 and
#  then run the subsequent lines, or just skip
#  down to line 83 and get the new values
####################################

#  If we express the confidence level as a 
#  percent then we say that that percent of the
#  confidence intervals that we generate 
#  using this methodology will contain the 
#  true mean.  That means, that at this point
#  in running the script, I do not know if the
#  95% confidence interval that we generated, 
#  namely (173.464, 213.299 ) does or does not
#  contain the true mean.  
#
#  Let us find the true mean and see if it is
#  in the interval.

true_mean <- mean( big_pop )
true_mean
#   yes it is!  

#  This has been an illustration, but let us 
#  go through the process 10000 times and 
#  see how many intervals that we generate this
#  way contain the true mean

#  first reset the confidence level and
#  sample size  just in case we want to change
#  them later

conf_level <- 0.95
samp_size <- 38

L3 <- 1:10000
for( i in 1:10000 ) {
  this_sample <- sample( big_pop, samp_size )
  this_ci <- ci_known( hold_pop_sd, samp_size,
                       mean( this_sample),
                       conf_level)
  if( this_ci[1] <= true_mean &
      true_mean <= this_ci[2] ) {
    L3[i] = "hit"}
  else {
    L3[i] = "missed"}
}
# see how we did
table( L3 )

#########
#  if we want we can do this again and we
#  can even change the values in lines 118 
#  and/or 119 if we want.