MATLAB HELP CODING

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%Before starting this section, make sure you have downloaded 'RandSel.m'

%and 'RandLim.m' from Canvas and put them in your

%current folder and/or add them to your path.

%Here we are going to conduct a Bootstrap analysis to test whether the

%difference in means between two vectors is significant.

%Start by defining two vectors, V1 and V2, as follows:

V1 = [19 6 25 27 29 8 22 13 18 14];

V2 = [18 32 36 14 24 26 19 38];

% Calculate the means of V1 and V2

% Use a 2-sample t-test to determine whether these means of V1 and V2

%are significantly different. Please include both the p-value and the 95%

%confidence interval of the difference between means in your answer.

% Now to begin a Bootstrap analysis, define an empty vector BMD which

%is going to contain a bunch of Bootstrap mean differences

% Make a for-loop that loops 10000 times. In each loop, create a

%Bootstrap sample of V1, a Bootstrap sample of V2, and store the

%difference between the means of the two Bootstrap samples in BMD

%

%Hint: Recall that each Bootstrap sample should have the same number of

%elements as the original sample

% Based on the Bootstrap analysis, is the difference between means

%of V1 and V2 significant? (Hint: find the 95% confidence interval of

%BMD and check if it crosses zero)

% How does your answer to 2.5 compare to your answer to 2.2?

function out = RandLim(n,lower,upper) % returns pseudo-random values drawn from a uniform distribution with lower % and upper limits LOWER and UPPER. % LOWER and UPPER can also be matrices of the same shape as rand(N) would % create % % N is a vector indicating the dimensions of the output: % N = [3] creates a 3x3 matrix % N = [1,3] creates a 1x3 matrix % N = [17,1,3] creates a 17x1x3 matrix % % LOWER and UPPER can be any number

% DN 2008-07-21 Wrote it % DN 2008-09-19 Support for vector lower and upper limits % DN 2016-08-25 Switched from deprecated nargchk to narginchk

narginchk(3,3);

r = rand(n); if (~isscalar(lower) && any(size(lower)~=size(r))) || (~isscalar(upper) && any(size(upper)~=size(r))) error('LOWER and UPPER must each be scalar or have the same shape as rand(N)') else out = lower + r.*(upper-lower); end

function sel = RandSel(in,n) % sel = RandSel(in,n) % if N is a scalar % randomly selects N elements from set IN, elements are replenished. % Output is of size 1xN % if N is a vector % randomly selects prod(N) elements from set IN, with replenishment. N % specifies the shape of the output, e.g., if N=[10 10 5], SEL will be % a 10x10x5 matrix % IN can be of any datatype and can have any number of dimensions and % elements

% DN 2008 wrote it % 2008-08-06 Updated it to support n selections from set (DN) % 2012-06-13 Can now also return shaped inputs through non-scalar n input (DN)

if isscalar(n) n = [1,n]; end

selind = ceil(RandLim(n,0,numel(in))); sel = in(selind);